MOTIVATIONS: Electroencephalography (EEG) is a non-invasive method that records brain electrical activity from scalp electrodes, offering millisecond temporal resolution but limited spatial detail due to sparse sensor layouts. RESULTS: We present DiBiMa-EEGSR, a bidirectional Mamba-2 diffusion framework for spatio-temporal EEG super-resolution that reconstructs high-resolution signals from standard low-density recordings without additional hardware. The method formulates super-resolution as conditional generative inference and integrates a diffusion process with a bidirectional state-space backbone to model long-range temporal dependencies with linear complexity. Conditioning on low-resolution inputs, electrode positions and task labels enables anatomically coherent and context-aware reconstruction. A one-step sampling strategy substantially reduces inference time while preserving fidelity. Across two public benchmarks, the approach improves reconstruction accuracy, spatial coherence and spectral preservation over convolutional, transformer-based and prior diffusion models in both spatial and temporal upsampling tasks, providing a scalable pathway toward high-resolution electrophysiological imaging. AVAILABILITY AND IMPLEMENTATION: Code to reproduce ablation experiments, training and evaluation of the proposed BiMa and DiBiMa EEGSR models are available at https://github.com/UgoLomoio/DiBiMa-EEGSR.git. Model weights are available at https://huggingface.co/Ugo96/DiBiMa-EEGSR while an interactive demo for EEG spatial super-resolution using our models can be found at https://huggingface.co/spaces/Ugo96/DiBiMa-EEGSR-Demo.

Drug resistance, the decrease in the effectiveness of a medication over time, is a major global threat for public health as it makes it harder and more expensive to fight against diseases, harmful microbial species such as bacteria and viruses. It is established that genes play a significant role in sensitivity to drugs. In this paper, we address the problem of establishing causality between transcription patterns of genes and drug resistance. Class separation based models can be used to provide an explainable solution for the causality definition for drug resistance. However, for$m$samples and$n$genes, the time and space complexities of the class separation problem are, respectively$O\left(m^{2} n^{2}\right)$and$O\left(n^{2}\right)$making it too costly to study this problem at whole genome scale. We develop an efficient implementation of the class separation model, named Hierarchical Class Separation Transformation (HCST), which solves this problem in$O\left(h n m^{2} k\right)$time, where$k$and$h$are user controlled parameters indicating the partition size for the gene set and gene set mixing limit, with$h k \ll n$, and space$O\left(k m+k^{2}\right)$. HCST allows solving the class separation problem at entire human genome scale in an efficient way, scaling in an efficient way (i.e., less than 2 minutes of running time). Our results demonstrate that HCST is scalable, robust, and can accurately identify genes which affect drug resistance. Code developed in this paper is available at https://github.com/richiebailey74/HCST.

Large language models (LLMs) have demonstrated remarkable versatility in natural language understanding and generation, yet their reliability in specialized medical domains remains uncertain. We focus on the use of lightwight and specialized LLMs instances on cardiological medical data corpora. We present a comparative evaluation of four general-purpose LLMs, i.e., ChatGPT, Gemini, Claude AI, and PerplexityAI trained on cardiology clinical data. We assess response quality on set of clinically relevant queries with a score in the [0,1] interval. Score were grouped in three classes: good (scores greater than 0.75), sufficient (scores in the interval 0.51-0.74), insufficient (scores less than 0.5). Results show that the lightweight model achieved the highest proportion of sufficient answers evaluated as sufficient (54%) and a greater share of answers evaluated as good (23%) than general-purpose counterparts, while maintaining a relatively low proportion of insufficient responses$(23 \%)$. In contrast, general-purpose models exhibited greater variability, alternating between highly accurate and critically lacking outputs, with PerplexityAI performing weakest overall. These findings suggest that targeted domain adaptation, even with lightweight architectures, can yield more stable and clinically reliable outputs than large general-purpose systems. The study underscores the potential of lightweight, specialized LLMs as trustworthy components in medical decision support frameworks, where consistency and factual grounding are paramount.

Using artificial intelligence (AI) offers opportunities to analyze medical images and to support early cancer detection. For instance, neural networks, in different implementation, can be used to analyze data image parts (i.e., voxels), defining a trained network useful for lung cancer nodules detection. We present our experience in designing and testing a transformerbased deep learning architecture, aiming to detect pulmonary cancer nodule candidates using 3D Computed Tomography (CT) images. The module also includes a preprocessing pipeline based on dynamic sampling of voxels extracted from images, to support data filtering and results explainability. The proposed architecture has been implemented, trained, and tested using the LUNA16 publicly available dataset. Experimental results proved both high effectiveness and competitive performance metrics across standard evaluations. Trained module can be used on a large CT dataset aiming to support clinicians in lung cancer early detection as well as to support in followup for lung cancer patients treatments. This work represent, indeed, results for preliminary applications in a research project (Advancing Lung Cancer Screening: Artificial Intelligence, Multimodal Imaging and Cutting-Edge Technologies for Early Detection and Characterization), conducted in collaboration with San Raffaele Hospital (Italy), Campus Biomedico University (Italy) and University Hospital of Salerno.

Emphysema is a hallmark of Chronic Obstructive Pulmonary Disease and an independent risk factor for lung cancer. Computed Tomography (CT) is the main diagnostic platform for identifying emphysema. In clinical practice, the quantitative assessment identifies emphysema as low attenuation areas under a specific cut-off threshold set to -950 Hounsfield Unit. Despite its wide adoption, this method lacks consensus on an optimal cut-off threshold and is prone to measurement variation, asking for new solutions that encompass this limitation. We propose a hybrid deep learning approach for emphysema classification that combines convolutional neural networks for local feature extraction with MAMBA's capability to model long-range dependencies. This fusion ensures a complementary feature representation, capturing both fine-grained and global contextual information. Furthermore, we demonstrate the effectiveness of self-supervised pretraining in domain-specific data, refining the weight configuration of the model to better align with the target distribution and improve its performance during supervised training. The results show on the SCAPIS public cohort that our hybrid model not only outperforms the traditional LAV950 method for emphysema quantification but also surpasses two well-established deep learning architectures. The code is available at: https://github.com/TrainLaboratory/Emphysema.

Electrocardiogram signals play a pivotal role in cardiovascular diagnostics, providing essential information on electrical hearth activity. However, inherent noise and limited resolution can hinder an accurate interpretation of the recordings. In this paper an advanced Denoising Convolutional Autoencoder designed to process electrocardiogram signals, generating super-resolution reconstructions is proposed; this is followed by in-depth analysis of the enhanced signals. The autoencoder receives a signal window (of 5 s) sampled at 50 Hz (low resolution) as input and reconstructs a denoised super-resolution signal at 500 Hz. The proposed autoencoder is applied to publicly available datasets, demonstrating optimal performance in reconstructing high-resolution signals from very low-resolution inputs sampled at 50 Hz. The results were then compared with current state-of-the-art for electrocardiogram super-resolution, demonstrating the effectiveness of the proposed method. The method achieves a signal-to-noise ratio of 12.20 dB, a mean squared error of 0.0044, and a root mean squared error of 4.86%, which significantly outperforms current state-of-the-art alternatives. This framework can effectively enhance hidden information within signals, aiding in the detection of heart-related diseases. • We defined a novel architecture based on autocencoders which is able to denoise and reconstruct high resolution copies of input low resolution ECG signals. • This unique approach that has not been previously applied to ECG signals. • We also present a deep validation of our approach against traditional and contemporary methods in terms of signal-to-noise ratio, mean squared error, and root mean squared error with those of other widely used ECG signal processing techniques. • The results consistently showed superior performance, further validating the effectiveness of our approach. • Given the increasing reliance on effective and efficient diagnostic techniques in medical practice, especially in cardiology, the findings of our study have significant practical implications.

Sex differences appear in healthy and pathological conditions and may influence sex-specific therapeutic responses. Understanding such differences is a key activity for developing precision medicine strategies. This study investigates sex differences in gene expression across 40 human tissues by applying a Differential Causal Network (DCN) analysis using data from the Genotype-Tissue Expression project. We identified sex-based DCNs that highlight distinct molecular mechanisms influencing both health and disease in men and women. For example, in pancreas tissue, genes associated with immune system show significant differences in their regulatory patterns between sexes, demonstrating a possible different response to diseases such as diabetes mellitus and cancer. Our findings provide valuable information on the biological underpinnings of sex differences, offering potential pathways for the development of precision medicine strategies.

Recently, the availability of many omics data source has given the rise of modelling biological networks for each individual or patient. Such networks are able to represent individual-specific characteristics, providing insights into the condition of each person. Given a set of networks of individuals, a network representing a particular condition (e.g., an individual with a specific disease) may be seen as an anomaly network. Consequently, the use of Graph Anomaly Detection techniques may support such analysis. Among the others, Generative Adversarial Networks present optimal performances in anomaly detection. This paper presents ADIN (Anomaly Detection in Individual Networks), a framework based on Generative Adversarial Attributed Networks (GAANs) for anomaly detection in convergence/divergence patients attributed networks. Preliminary results on networks generated from computational biology gene expression data demonstrate the effectiveness of our approach in detecting and explaining bladder cancer patients.

Studying infection-related diseases, such as those associated with cardiological surgical interventions, often requires the acquisition and analysis of heterogeneous data, including bioimages, microbiological data, and blood analytes. Multidisciplinary collaboration among clinicians and specialists is also essential for effective data management and to develop strategies for the prevention and treatment of infections. Acquiring and analyzing data for clinical studies is a vital approach to preventing infectious diseases. In this context, the REDCap platform provides a comprehensive solution for collecting, managing, and analyzing clinical and hospitalization information. REDCap enables data entry validation, automated reporting, and data integration, which facilitates data management and ensures data quality.This contribution describes a REDCap-based method to support predictive clinical studies on infective endocarditis. The application aims to advance clinical research on this disease, improving understanding and fostering the development of effective treatment strategies, while assisting clinicians in defining cardiac infection prevention methods.

Sarcopenia is a clinical condition marked by loss of muscle mass and strength, leading to reduced mobility and quality of life. Accurate identification and quantification of muscle mass are essential for the timely diagnosis and treatment of sarcopenia. To calculate muscle volumes and sarcopenia indexes, segmentation techniques are required on CT images, helping to adjust treatments for chronic diseases. However, evaluating muscle volumes and thus determining sarcopenia indexes currently is highly dependent on manual image segmentation by human operators. We propose a deep learning architecture for automatic muscle mass segmentation, integrating DeepLabv3+ and U-Net3+ models for 2D and 3D segmentation, respectively. These models have been tested on available datasets and combined using an ensemble learning approach to enhance predictive accuracy. This proposed architecture can be integrated into clinical workflows for the assessment of sarcopenia, increasing the reliability and efficiency of image-based diagnoses.

BACKGROUND: The rewiring of molecular interactions in various conditions leads to distinct phenotypic outcomes. Differential network analysis (DINA) is dedicated to exploring these rewirings within gene and protein networks. Leveraging statistical learning and graph theory, DINA algorithms scrutinize alterations in interaction patterns derived from experimental data. RESULTS: Introducing a novel approach to differential network analysis, we incorporate differential gene expression based on sex and gender attributes. We hypothesize that gene expression can be accurately represented through non-Gaussian processes. Our methodology involves quantifying changes in non-parametric correlations among gene pairs and expression levels of individual genes. CONCLUSIONS: Applying our method to public expression datasets concerning diabetes mellitus and atherosclerosis in liver tissue, we identify gender-specific differential networks. Results underscore the biological relevance of our approach in uncovering meaningful molecular distinctions.

There is increasing evidence that many molecular processes exhibit differences with age and sex. Such differences produce also differences in the insurgence and progression of many complex diseases. For instance, demographic data on the insurgence of comorbidities of mellitus diabetes, on the lethality of COVID-19, and on some cancers shows differences between sex and age groups. Therefore, the growing interest in such areas requires the management of related data as well as the development of algorithms and tools for the analysis. The availability of omics data annotated with metadata related to age and sex is mandatory for building the analysis pipeline. The number of databases containing data related to age and sex is henceforth growing. We here show some databases and tools storing such data. Finally, future research directions are highlighted.

Patient triage plays a crucial role in healthcare, ensuring timely and appropriate care based on the urgency of patient conditions. Traditional triage methods heavily rely on human judgment, which can be subjective and prone to errors. Recently, a growing interest has been in leveraging artificial intelligence (AI) to develop algorithms for triaging patients. This paper presents the development of a novel algorithm for triaging patients. It is based on the analysis of patient data to produce decisions regarding their prioritization. The algorithm was trained on a comprehensive data set containing relevant patient information, such as vital signs, symptoms, and medical history. The algorithm was designed to accurately classify patients into triage categories through rigorous preprocessing and feature engineering. Experimental results demonstrate that our algorithm achieved high accuracy and performance, outperforming traditional triage methods. By incorporating computer science into the triage process, healthcare professionals can benefit from improved efficiency, accuracy, and consistency, prioritizing patients effectively and optimizing resource allocation. Although further research is needed to address challenges such as biases in training data and model interpretability, the development of AI-based algorithms for triaging patients shows great promise in enhancing healthcare delivery and patient outcomes.

Dementia is a major issue for healthcare systems worldwide, necessitating the development of creative and effective strategies for its management. This paper examines the use of Artificial Intelligence (AI) technologies in caring for and managing people with dementia. AI-driven solutions can improve diagnosis, personalize care, optimize medication management, and reduce the burden on caregivers. This paper discusses the implementation of an AI-based framework for creating videos related to people’s memories to support train-therapy or travel-therapy, a non-pharmacological intervention for Alzheimer’s disease patients.

Healthcare access (HA) is a multi-dimensional concept that includes health services availability and accessibility for the populations. These services should be determined by population healthcare needs, especially for vulnerable populations. Digital healthcare service became more important to facilitate the access to medical care by vulnerable people and by citizens in general. Digital health and technologies have provided many online e-services to address social and healthcare services.In this contribution, we propose the implementation of an innovative platform to support and manage the access to health and social services for vulnerable people. Two different use cases have been proposed to demonstrate the application of this platform to different healthcare contexts. The results shows the benefits of using the platform in terms of request management times and reduction of hospitalization.

The quality and traceability of agricoltural and food products (indicated as agri-food) represents an important task for industries to focus on environments and wellness targets. In the context of milk and vegetable production processes, it is no possible to monitor and control animals behaviour, environmental conditions, and overall quality affecting these productions. Accurate and explainable predictions of quantities, as well as food properties qualities, is relevant for marketing and planning action in agri-food companies thus to in obtaining more efficient higher-quality productions and contribute to citizens wellness.We here report examples and experiences of machine learning algorithms application to evaluate and predict quantity and frequency of production in an large south of Italy farm. Data are extracted from a tracking system storing all production phases, i.e.: (i) from fruits plants to storage, cold maintaining and transportation, and (ii) cows management, fresh milk analysis and packaging. The here proposed experience contributes to evaluate and predict quantity and frequency of production, aiming to support farms in product planning and production phases.

MOTIVATION: Studying ageing effects on molecules is an important new topic for life science. To perform such studies, the need for data, models, algorithms, and tools arises to elucidate molecular mechanisms. GTEx (standing for Genotype-Tissue Expression) portal is a web-based data source allowing to retrieve patients' transcriptomics data annotated with tissues, gender, and age information. It represents the more complete data sources for ageing effects studies. Nevertheless, it lacks functionalities to query data at the sex/age level, as well as tools for protein interaction studies, thereby limiting ageing studies. As a result, users need to download query results to proceed to further analysis, such as retrieving the expression of a given gene on different age (or sex) classes in many tissues. RESULTS: We present the GTExVisualizer, a platform to query and analyse GTEx data. This tool contains a web interface able to: (i) graphically represent and study query results; (ii) analyse genes using sex/age expression patterns, also integrated with network-based modules; and (iii) report results as plot-based representation as well as (gene) networks. Finally, it allows the user to obtain basic statistics which evidence differences in gene expression among sex/age groups. CONCLUSION: The GTExVisualizer novelty consists in providing a tool for studying ageing/sex-related effects on molecular processes. AVAILABILITY AND IMPLEMENTATION: GTExVisualizer is available at: http://gtexvisualizer.herokuapp.com. The source code and data are available at: https://github.com/UgoLomoio/gtex_visualizer.

Magnetic Resonance Images (MRI) allow to extract meaningful structural information. Machine learning and neural network based algorithms are used to analyze such images, to extract features and to identify anomalies related to diseases. To perform anomaly detection tasks in MR images of the human brain, we propose the use of the Variational AutoEncoder (VAE) method. A VAE is a deep-learning method able to compress and reconstruct the original image through well-defined functions aiming to extract only significant features that are used to identify abnormal pattern. In this contribution, we present a tool based on VAE method for the identification and annotation of brain lesions in MRI aiming to support physicians in the detection of anomalies. Moreover, a MongoDB database is also used to store the data and manage the annotations.

Network medicine helps to shed light insight many chronic diseases by offering useful information about mechanistic information from omic data sets. Type 2 diabetes mellitus (T2DM) is one of the major challenges in medical research. it has been demonstrated that the odds of comorbidities is different considering age and sex of patients. Therefore the use of a framework such as a system and network approach may be useful to shed light into uncertainties related to sex, age effects and comorbidity. We first selected from T2Dico database the list of genes related to comorbidities. Then we first extracted networks of proteins connecting them. In parallel we analysed the pattern of expression of them considering both age and sex as factors, stored into the GTEx database. Preliminary results showed the action of few genes and the biological validation is currently carried out.

Recently there is a growing interest for the study of the molecular basis of ageing processes and on the differences among genders. These studies require many data, models and tools for inferring molecular mechanisms. Among the others, the Genotype-Tissue Expression (GTEx) database is one of the prominent resources for the analysis of expression data related to tissues, sex and age. The current version of the database has a lot of querying interfaces that enable many analysis centred on the expression of genes on tissues. Despite this, the database lacks on the analysis at sex/age level, thus the researcher has to download data and then write queries by hand (e.g. for retrieving the expression of a given gene on different age-class in many tissues). It also lacks on the integration with existing protein interaction data. Therefore, the need for the introduction of tools enabling easy access and powerful analysis capabilities (i.e. state of the art network based analysis and integration), arises. We here present NOMA-DB, a framework for ageing studies based on an extension of the GTEx database that enable easy querying at sex/age level, network based analysis. The framework is based on wrapping the GTEx database and on building an application logic level on top of existing data. The current version enables the analysis of genes by tissue, gene and age, thus it may be used in potentially future directions of analysis towards better comprehension of aging/sex-related molecular processes based on the analysis of expression data.

Brain network analysis represents an effective tool useful to explore the connectivity patterns to uncover related features and phenomena concerning different brain functions and diseases, such as the epilepsy. According to the clinical-psychological and neurological studies, signal patterns for network analysis mainly include data coming from functional magnetic resonance imaging (fMRI), positron emission tomography (PET), magnetoencephalogram (MEG), and electroencephalogram (EEG). High spatial-resolution fMRI and high temporal-resolution EEG are non-invasive and common techniques. EEG is quite low cost and, in many cases, EEG signal can provide important physiological information about the activity of the human brain which can be used to identify and detect epilepsy. In this paper we propose a framework for graph analysis of EEG functional connectivity networks, using EEG of epileptic subjects to build complex networks. Our aim is to provide a method to identify epilepsy in the early stages supporting physician in performing appropriate measures to prevent accidental consequences and to ensure health patient.

Medical image fusion allows to combine multiple images from single or multiple imaging modalities to improve the imaging quality. It supports physicians in performing more accurate clinical decisions and analysis based on medical images. Hybrid imaging with FDG PET/CT is became a standard in the day-to-day clinical practice for management of cancer because it combines the subtle biologic changes detected by FDG PET with the anatomic information offered by CT scans.In this context, we consider hybrid imaging with FDG PET/CT to propose a methodology aiming to use the fusion CT and PET images to evaluate drug administration in patients undergoing treatment.

Quantitative analysis of PET images is a clinical common practice. It is used to estimate the input function of 18F-FDG tracer in order to study a physiological process and to evaluate the response to a treatment. It allows the evaluation of coronary artery pathologies, as well as metabolic syndrome associated to cardiovascular diseases. We propose a method for analyzing the dynamic PET cardiac images aiming to assess the progress of glucose metabolism on large vessels as the aorta one. The aim is to study the relation among drug dosage with metabolic syndrome. Indeed, the aim is to correlate the glucose metabolism values (specifically MRGlu - Glucose Metabolic Rate) quantified in the aorta and in the left ventricle, by using PET dynamic images. The measures are presented and proposed for clinical drug validations.

In the recent decade, the amount of digital information recorded in electronic health records (EHRs) has increased dramatically. EHRs are no longer used to store basic patient information and administrative activities, but they can now store a wide range of data, from the patient's medical history to images. The issue currently is not so much gathering data as it is analyzing it, that is, converting data into knowledge, conclusions, and actions. The use of innovative technology instruments, such as artificial intelligence (AI) algorithms, to support medical inter-disciplinary collaboration among different teams, geographically distributed in the network, extracting useful information from EHRs, and integrating data from various data sources is a crucial, but still difficult task. SIMPATICO 3D (Sistema Informativo Medico PATologIe COmplesse) is a system that assists scientists and clinicians by offering tools for managing, organizing, analyzing, and disseminating medical data. The project SIMPATICO 3D originates from a collaboration between the software house eway Enterprise Business Solutions, the DIMES Department of the University of Calabria and the DMSC Department of the University Magna Graecia of Catanzaro and it has been selected for funding under the FESR 2014/2020.

Cell surface proteins have been used as diagnostic and prognostic markers in cancer research and as targets for the development of anticancer agents. Many of these proteins lie at the top of signaling cascades regulating cell responses and gene expression, therefore acting as 'signaling hubs'. It has been previously demonstrated that the integrated network analysis on transcriptomic data is able to infer cell surface protein activity in breast cancer. Such an approach has been implemented in a publicly available method called 'SURFACER'. SURFACER implements a network-based analysis of transcriptomic data focusing on the overall activity of curated surface proteins, with the final aim to identify those proteins driving major phenotypic changes at a network level, named surface signaling hubs. Here, we show the ability of SURFACER to discover relevant knowledge within and across cancer datasets. We also show how different cancers can be stratified in surface-activity-specific groups. Our strategy may identify cancer-wide markers to design targeted therapies and biomarker-based diagnostic approaches.

MOTIVATION: Protein Contact Network (PCN) is a powerful method for analysing the structure and function of proteins, with a specific focus on disclosing the molecular features of allosteric regulation through the discovery of modular substructures. The importance of PCN analysis has been shown in many contexts, such as the analysis of SARS-CoV-2 Spike protein and its complexes with the Angiotensin Converting Enzyme 2 (ACE2) human receptors. Even if there exist many software tools implementing such methods, there is a growing need for the introduction of tools integrating existing approaches. RESULTS: We present PCN-Miner, a software tool implemented in the Python programming language, able to (i) import protein structures from the Protein Data Bank; (ii) generate the corresponding PCN; (iii) model, analyse and visualize PCNs and related protein structures by using a set of known algorithms and metrics. The PCN-Miner can cover a large set of applications: from clustering to embedding and subsequent analysis. AVAILABILITY AND IMPLEMENTATION: The PCN-Miner tool is freely available at the following GitHub repository: https://github.com/hguzzi/ProteinContactNetworks. It is also available in the Python Package Index (PyPI) repository.

MOTIVATION: In bioinformatics, complex cellular modeling and behavior simulation to identify significant molecular interactions is considered a relevant problem. Traditional methods model such complex systems using single and binary network. However, this model is inadequate to represent biological networks as different sets of interactions can simultaneously take place for different interaction constraints (such as transcription regulation and protein interaction). Furthermore, biological systems may exhibit varying interaction topologies even for the same interaction type under different developmental stages or stress conditions. Therefore, models which consider biological systems as solitary interactions are inaccurate as they fail to capture the complex behavior of cellular interactions within organisms. Identification and counting of recurrent motifs within a network is one of the fundamental problems in biological network analysis. Existing methods for motif counting on single network topologies are inadequate to capture patterns of molecular interactions that have significant changes in biological expression when identified across different organisms that are similar, or even time-varying networks within the same organism. That is, they fail to identify recurrent interactions as they consider a single snapshot of a network among a set of multiple networks. Therefore, we need methods geared towards studying multiple network topologies and the pattern conservation among them. Contributions: In this paper, we consider the problem of counting the number of instances of a user supplied motif topology in a given multilayer network. We model interactions among a set of entities (e.g., genes)describing various conditions or temporal variation as multilayer networks. Thus a separate network as each layer shows the connectivity of the nodes under a unique network state. Existing motif counting and identification methods are limited to single network topologies, and thus cannot be directly applied on multilayer networks. We apply our model and algorithm to study frequent patterns in cellular networks that are common in varying cellular states under different stress conditions, where the cellular network topology under each stress condition describes a unique network layer. RESULTS: We develop a methodology and corresponding algorithm based on the proposed model for motif counting in multilayer networks. We performed experiments on both real and synthetic datasets. We modeled the synthetic datasets under a wide spectrum of parameters, such as network size, density, motif frequency. Results on synthetic datasets demonstrate that our algorithm finds motif embeddings with very high accuracy compared to existing state-of-the-art methods such as G-tries, ESU (FANMODE)and mfinder. Furthermore, we observe that our method runs from several times to several orders of magnitude faster than existing methods. For experiments on real dataset, we consider Escherichia coli (E. coli)transcription regulatory network under different experimental conditions. We observe that the genes selected by our method conserves functional characteristics under various stress conditions with very low false discovery rates. Moreover, the method is scalable to real networks in terms of both network size and number of layers.

Patient medical records contain several types of data, such as images, signals, or textual data. The integration of such data on a single system provides the possibility to select the clinical data of interest and then to choose the information extraction operation to be performed on such data. Formulating diagnoses of complex diseases is a challenging task, which is often the key to the precise identification of the correct therapies. Hence, a uniforming environment for data clinical staging, in which physicians can perform data annotations and images manipulation could be of great help in order to convey relevant information forming the clinical summary of a patient with great precision and detail. In this work we present a semi-automatic tool for clinical data annotation aiming to be a decision support system.

Parotid, sub-mandibular and several glands are salivary glands and they are responsible of saliva secretion. Moreover, they can present diseases related to neoplastic or inflammatory lesions. The parotid is the biggest salivary gland and it is very important because it is often the site of neoplastic formations. The identification and the diagnosis of salivary disorders can be performed by physiological inspection correlated with the analysis of clinical data and images. In this context, diagnostic imaging is able to support early detection, helping physicians in clinical examination and diagnosis. Here, we present a semi-automatic tool for the annotation and integration of clinical images aiming to be a decision support system and to train automatic lesion classifiers.

The salivary glands are exocrine glands aiming to make, modify and secrete saliva into the oral cavity. These glands can present diseases related to inflammatory or neoplastic lesions. Generally, diagnosis of salivary gland disorders is performed by physiological inspection supported by clinical data and images analysis. We focus on a methodology to annotate and integrate clinical information and bioimages to support physicians in disease examination and diagnosis.

MOTIVATION: The outbreak of novel severe acute respiratory syndrome coronavirus (SARS-CoV-2, also known as COVID-19) in Wuhan has attracted worldwide attention. SARS-CoV-2 causes severe inflammation, which can be fatal. Consequently, there has been a massive and rapid growth in research aimed at throwing light on the mechanisms of infection and the progression of the disease. With regard to this data science is playing a pivotal role in in silico analysis to gain insights into SARS-CoV-2 and the outbreak of COVID-19 in order to forecast, diagnose and come up with a drug to tackle the virus. The availability of large multiomics, radiological, bio-molecular and medical datasets requires the development of novel exploratory and predictive models, or the customisation of existing ones in order to fit the current problem. The high number of approaches generates the need for surveys to guide data scientists and medical practitioners in selecting the right tools to manage their clinical data. RESULTS: Focusing on data science methodologies, we conduct a detailed study on the state-of-the-art of works tackling the current pandemic scenario. We consider various current COVID-19 data analytic domains such as phylogenetic analysis, SARS-CoV-2 genome identification, protein structure prediction, host-viral protein interactomics, clinical imaging, epidemiological research and drug discovery. We highlight data types and instances, their generation pipelines and the data science models currently in use. The current study should give a detailed sketch of the road map towards handling COVID-19 like situations by leveraging data science experts in choosing the right tools. We also summarise our review focusing on prime challenges and possible future research directions. CONTACT: [email protected], [email protected].

BACKGROUND: Representations of the relationships among data using networks are widely used in several research fields such as computational biology, medical informatics and social network mining. Recently, complex networks have been introduced to better capture the insights of the modelled scenarios. Among others, dual networks (DNs) consist of mapping information as pairs of networks containing the same set of nodes but with different edges: one, called physical network, has unweighted edges, while the other, called conceptual network, has weighted edges. RESULTS: We focus on DNs and we propose a tool to find common subgraphs (aka communities) in DNs with particular properties. The tool, called Dual-Network-Analyser, is based on the identification of communities that induce optimal modular subgraphs in the conceptual network and connected subgraphs in the physical one. It includes the Louvain algorithm applied to the considered case. The Dual-Network-Analyser can be used to study DNs, to find common modular communities. We report results on using the tool to identify communities on synthetic DNs as well as real cases in social networks and biological data. CONCLUSION: The proposed method has been tested by using synthetic and biological networks. Results demonstrate that it is well able to detect meaningful information from DNs.

The papers in this special section focus on innovative data analysis methods for biomedicine. Biomedical engineers as well as bioinformaticians have been working on data structures and methods to manage huge quantities of data that can be stored and efficiently accessed in both local data storage and cloud data structures. Recent activities and experiences reported in the literature reflect the new developments on how to access, process, and transform data into solutions for clinicians and patients. This special issue has been designed during the COVID-19 pandemic, at the end of 2019, with a focus to present some of the best papers at ACM-BCB 2019 (Buffalo, NY).

The efficient management and analysis of patient data enrolled in clinical studies is a critical factor for both supporting data management and knowledge discovery from data. Recent trends in literature present many approaches that demonstrate that the integration of multiple data sources (e.g. biochemical parameters, geographical data as well as the behaviour of patients into social networks) may improve the quality of findings. Moreover, the collection of such data may enable the development of a tailored intervention for precision medicine. All these aspects rely on the design and development of novel solutions for data management, storing and consequently, analysis. We here report the design and development of a prototype for data management and sharing introduced during a collaboration of Bioinformatics Laboratory, the Fisiopatology Unit and the University Hospital of Catanzaro. Our findings are currently under the validation of the clinicians.

COVID-19 (SARS-CoV-2) is a pandemic disease diffused throughout the world. COVID-19 is usually identified by applying Reverse transcriptase-polymerase chain reaction (RT-PCR) analysis on swab tests. The high rate of diffusion of the disease caused many problems related to the managing part of limited healthcare resources such as Intensive Care Units (ICUs) services. Assessing the real number of infected as well as early identification of the more infected zones have been defined as a relevant issue to treat pandemic. COVID-19 infected citizens are identified by swab test applied on suspected cases as well as people that have been in touch with affected ones. For these reasons, recognised numbers of COVID-19 affected patients are significantly lower than real ones. We investigate the number of COVID-19 infections and the number of deaths, through Italian regions by comparing these data with respect to diseases caused by similar viruses. We assess several infections having a higher rate of dissemination than the ones currently measured. We focus on the characterisation of the pandemic diffusion by estimating the infected number of patients versus the number of death. We believe that our model can support the healthcare system to react as COVID-19 infection rate increases.

Physical therapy and rehabilitation therapy aim to support patients in dealing with the consequences of their and physical impairments in daily activities. The recent developments in biomedical sensors combined with the wireless network infrastructure will deeply transform healthcare systems and help physicians in designing better and more precise therapies with faster feedback from patients in terms of health-related measured data. Furthermore, these new systems will enable distributed healthcare services for remote patients who may live far away from health structures or who may have movement impairment. Efficiently monitoring or acquiring data from a large number of patients will cause improvements during rehabilitation and help in early diagnoses together with reducing the costs in the healthcare system with more effective prevention. We present a programmable rehabilitation device which can be useful to evaluate patients' performances in a set of physiotherapy exercises designed to evaluate subjects by neurophysiological impairments which slow down some types of movements. The tool is able to support the definition of rehabilitation exercises involving upper limbs and hands. The presented device assesses the responsiveness and movement capacity of patients undergoing physiotherapy aiming to test and measure the mobility, strength and functional ability of the hand during prone supination exercises.

Epidemiological models are relevant to study and analyze clinical as well as environmental and behavioural data, useful to support health studies. The target is to perform epidemiological analysis producing fast and reliable data access useful to guide prevention and curing processes. This is currently true in pandemic emergency as the current Covid-19 context. Epidemiological models should support in the early identification of pandemic phenomena and in making available data set for studying more accurate drug-based strategy for vaccines or virus containment.In this contribution we present an epidemiology database which integrates different types of clinical data to support research, follow-up and patient monitoring. The idea starts from an hospital databases cooperation integration where virus available data have been integrated to support statistical based studies. Starting from an available database containing 5 years data of infection related viruses (such as HPC, hepatitis) and patient anonymous data, the proposed system provide an integrated data access able to (i) extracting data filtered by means of clinical hypothesis based on patient profiles, environment and drugs and (ii) allowing to build large scale geographical data mappings in order to study correlations among chronic infection diseases and their relations with upcoming pandemic phenomena. Even if the application is in its infancy, the application is relevant with high very important applications.

Prevention is essential to counteract the onset of cancer. The analysis of clinical and environmental data as well as their integration are basic topics to prevent chronic diseases, especially neoplasms, and to identify the correlations between cancer and environmental factors. The proposed contribution aims to acquire, analyze and integrate clinical and geographical data to evaluate their possible correlations. A GIS application is here reported to correlate TSH (Thyroid-Stimulating Hormone) values with environmental data as case study.

Wellness can be related to the prevention of diseases by means of ensuring the quality of products and is an important challenge in food industry. To this end, food traceability has become a priority in the industry in order let the final users to verify which production phases the food went through. Furthermore it gives domain experts the opportunity to trace defect or issues in the production workflow when problems arise. The proposed information system aims to track the production process of milk and vegetable products. This system is useful both for producers than consumers, giving them a complete tool for food traceability. It provides data management and processing in order to check each production step, making traceability a simpler and more efficient process. Information about raw materials, nutritional facts and activities is readily available and guarantees a transparent and secure supply chain.

Artificial Intelligent Systems are increasingly used to support early diagnosis of multiple relevant diseases. The spread of these systems is boosted by the application of machine learning techniques on datasets (also in the form of videos and images) obtained from different information sources. A key role is played by artificial vision systems that are in charge of reasoning on data acquired from different devices, including smartphones. The facility to disseminate and share information let to the globalization of medical protocols previously used just in some world's areas. This is the case of tongue inspection, widely used in Traditional Chinese Medicine (TCM) to perform a diagnosis, which allows physicians to obtain useful indications on the state of internal organs by observing the color and the consistency of patient's tongue. The current interest in tongue's image analysis is also motivated by the possibility of performing a first self-analysis on a possible disease suggesting further medical investigation. The paper is a non-exhaustive overview of the features most frequently used in artificial vision systems contextualized to tongue analysis. It highlights shortcomings in some of the existing studies and provides insights for future research. Our work aims to provide a unifying view that can support the researchers working on Tongue Colored Image Analysis.

Correct interpretation of images may be crucial for early disease detection. A growing number of medical instruments are image-oriented and produce a large quantity of image data, typically in the DICOM format, which contain spatio-temporal features together with alpha-numeric information regarding patients. Dealing with this high-dimensional datasets is a complex and time-consuming task. In addition the diffusion of smartphones and tablets requires the development of technological features enabling the medical team to check on helthcare processes on-the-go and freely access and send image and data for case analysis and collaborative diagnostic. This paper presents SIMPATICO 3D (Sistema Informativo Medico PATologIe COmplesse) a system supporting scientists and physicians by providing facilities for case studies analysis and diagnostic imaging in a shared virtual environment and details the features of SIMPATICO 3D Mobile (standing for Evolution Imaging System 3D for Mobile), that extends SIMPATICO 3D with dedicated functions for the mobile environment.

Extracting morphological features from DICOM images is useful to obtain numerical anatomic values for population-wide studies. Currently software tools on medical devices are able to extract some parameters that can indicate the presence of diseases. Nevertheless, there still is a lot of not exploited information contained in images which can be useful for research as well as to characterize human behavior. For instance, measures for lung volume compared with reference data sets can be studied starting from clinical images.

We present an application of a Multiple Instance Learning (MIL) approach to image classification. In particular we focus on a recent MIL method for binary classification where the objective is to discriminate between positive and negative sets of points. Such sets are called bags and the points inside the bags are called instances. In the case of two classes of instances (positive and negative), a bag is defined positive if it contains at least a positive instance and it is negative if it contains only negative instances. For such kind of problems there exist in literature two different approaches: the bag-level approach and the instance level approach. While in the former the total entity of each bag is considered, in the latter a classifier is obtained on the basis of the characteristics of the instances, without looking at the whole entity of each bag. The presented method is an instance-level approach and it is based on the application of the Lagrangian relaxation technique to a Support Vector Machine (SVM) type model. Preliminary numerical tests are discussed on a set of simple grey-level images.

Coronary artery disease (CAD) is the most common cause of death globally. Typically, it occurs when part of the coronary artery develops atherosclerosis, which causes the narrowing of the vessel lumen. The decision to be taken to treat the stenotic artery vessel is often influenced by several factors. The aim of this study is to develop an application (app) to provide support to the physician in decision-making. The app has been developed with an Application Builder based on Java, as a result of the previous simulation of computational fluid dynamics (CFD). By using a standard coronary anatomy, through the app it is possible to vary the degree of the stenosis to obtain information about the hemodynamic variations of not easily accessible parameters.

It is widely demonstrated that cardiovascular risk is reversible, that the reduction of major risk factors leads to a reduction in events, and that less serious events will occur in the future. One of the best goals in the clinical field is to help reduce the risk of subsequent cardiovascular events, thus improving the quality of life and positively influencing survival through the benefits of physical exercise. The first activity that a patient has to follow consists of a personalized prescription of physical reconditioning sessions by the physician. This activity can be highly individualized, with a correct mental algorithm of overall evaluation, based on manifold clinical, prognostic, metabolic, psychological and of physical ability variables. The Decision Support System (DSS) is a software system that provides support for the strategic decision. The purpose of this work was to plan and develop a DSS able to estimate cardiovascular risk and to optimize the therapeutic processes of physical reconditioning in the field of intensive cardiovascular rehabilitation. In this way it is possible to provide assistance to the above-mentioned decisions, thus helping improve the prescriptive process to be undertaken.

Computer based support for clinical and health-related procedures is growing in the last decades. However, the vast majority of information systems adopted in health structures are legacy systems, which do not often allow to export data easily and also are usually desktop oriented. A growing number of medical instruments are image-oriented and produce a large quantity of image data, typically in the DICOM format, which contain spatio-temporal features together with alpha-numeric information regarding patients. Dealing with this high-dimensional datasets is a complex and time-consuming task. In addition the diffusion of smartphones and tablets requires the development of technological features enabling the medical team to check on helthcare processes on-the-go and freely access and send image and data for case analysis and collaborative diagnostic. This paper presents eIMES 3D Mobile (standing for Evolution Imaging System 3D for Mobile), a system which is based on the eIMES 3D system and supports clinicians for images studies with dedicated functions for the mobile environment. The tool has been developed within a project called ReCaTuR for RAre Cancer Network (i.e., Network of Rare Cancer), aiming to define a network for the management, organization and distribution of medical information. Moreover, it has been implemented following the specifications by the oncology department of an Italian Hospital. eIMES 3D allows to start a medical interdisciplinary collaboration among different teams, geographically distributed in the network, so that obtaining the integration of skills, expertize, knowledge and experiences with the final aim of clinical case resolution. eIMES 3D provides an hardware infrastructure that allows to connect multiple devices, as well as to create workstations (WorkSpaces) that independently and asynchronously can request information to the central database containing the 3D imaging data. Il also allows to share information among a network of mobile devices. The ability to build plug-in modules enables to easily implement new features in eIMES 3D Mobile, thus ensuring its further development and its sustainability.

Genes participating in a common module may cause clinically similar diseases and shares the common genetic origin of their associated disease phenotypes. Identifying such modules may be helpful in system level understanding of biological and cellular processes or their disruption caused in associated diseases. The choose dofthe appropriate method for gene selection is a difficult task. In this work we discuss and compare selective module finding methods.

Electroencephalography (EEG) is a technique for the acquisition of electrical brain signals. In recent years the increase of information acquired from signal analysis has generated a large amount of data; therefore, the development of tools for analysis has become necessary. In this paper, the mEEG prototype for EEG data managing is presented. It offers a user-friendly communication solution to exchange data between physicians and biomedical engineers. Features can be used for: (i) perform a fast diagnoses; (ii) show reports about clinical information; (iii) store and retrieve neurological data.

High throughput experimental platforms and diagnostic equipments available in clinical settings and in research laboratories, such as magnetic resonance imaging, microarray, mass spectrometry and next-generation sequencing, are producing an increasing volume of clinical and omics data. Moreover, Electronic Patients Records (EPRs), eHealth systems, personal mobile sensors and Social Networks are collecting an overwhelming volume of health and life style data that may be integrated with clinical data and more and more is used for the real-time monitoring of patient's health. This poses new issues in terms of secure data storage, effective models for data integration, efficient algorithms for data analysis, new models for health monitoring, that may be addressed, among the others, using high performance computing solutions. Parallel computing and Cloud Computing may offer efficient and scalable solutions in an orthogonal way. In fact, parallel, bioinformatics software, that exploit off-the-shelf high performance computers, may be used to preprocess and analyze omics data at a lower layer, for instance to highlight genetic variation associated with complex diseases. On the other hand, Cloud Computing offers large scale data storage, data sharing services, on-demand anytime and anywhere access to resources and applications, for the realization of elastic and scalable applications and services. Motivated by the increasing use of parallel computing and cloud computing in life sciences, in this paper we survey both parallel bioinformatics algorithms for the parallel preprocessing and statistical and data mining analysis of omics data, as well as Cloud-based healthcare and biomedicine services and systems for large scale applications. Moreover, the paper underlines main issues and problems related to the use of such platforms for the storage and analysis of health data, with special focus to the security and privacy of patients data, that are particularly important in fields such as personalized medicine. Finally, the paper presents some case studies about the parallel and distributed modelling and simulation in medicine and biology.

BACKGROUND: Recently the study of the complex system of connections in neural systems, i.e. the connectome, has gained a central role in neurosciences. The modeling and analysis of connectomes are therefore a growing area. Here we focus on the representation of connectomes by using graph theory formalisms. Macroscopic human brain connectomes are usually derived from neuroimages; the analyzed brains are co-registered in the image domain and brought to a common anatomical space. An atlas is then applied in order to define anatomically meaningful regions that will serve as the nodes of the network - this process is referred to as parcellation. The atlas-based parcellations present some known limitations in cases of early brain development and abnormal anatomy. Consequently, it has been recently proposed to perform atlas-free random brain parcellation into nodes and align brains in the network space instead of the anatomical image space, as a way to deal with the unknown correspondences of the parcels. Such process requires modeling of the brain using graph theory and the subsequent comparison of the structure of graphs. The latter step may be modeled as a network alignment (NA) problem. RESULTS: In this work, we first define the problem formally, then we test six existing state of the art of network aligners on diffusion MRI-derived brain networks. We compare the performances of algorithms by assessing six topological measures. We also evaluated the robustness of algorithms to alterations of the dataset. CONCLUSION: The results confirm that NA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven comparison of connectomes. The analysis shows MAGNA++ is the best global alignment algorithm. The paper presented a new analysis methodology that uses network alignment for validating atlas-free parcellation brain connectomes. The methodology has been experimented on several brain datasets.

Electronic medical records (EMRs) store data related to patients information enrolled during their stay in health structures. Data stored into EMRs span from data crawled from biological laboratories to textual description of diseases and diagnostic device results (e.g., biomedical images). Each EMR is related to a diagnosis related group (DRG) record. A DRG record is a record associated with a citizen that has been cured in a hospital. It contains a code, called major diagnostic category (MDC), which summarizes the treated disease and allows to reimburse costs related to patient treatments during his staying in health structures. DRGs are used for administrative process (e.g., costs and reimbursement management) as well as disease monitoring. Associating diagnostic codes with external information (such as environmental and geographical data) and with information filtered from EMRs (e.g., biological results or analytes values) can be useful to monitor citizens wellness status. We propose a methodology to analyze such data based on a multistep process. First, we cross reference data by using a semantics-based clustering procedure, extract information from EMRs, and then, cluster them by looking for similar patterns of diseases. Then, biological records in each disease cluster are analyzed to evaluate intracluster similarity by selecting analytes typologies and values. Finally, biological data is related to diagnosis codes and geometrically projected in areas of interest in order to map calculated outlier patients. We applied the methodology on two case studies: 1) diagnosis codes and biochemical analytes of 20 000 biological analyses about hospitalized patients during one observation year and 2) the correlation between cardiovascular diseases and water quality in a southern Italian region. Preliminary findings show the effectiveness of our method.

Long non-coding RNAs (lncRNAs) are molecules more than 200 nucleotides involved in several biological processes. Next Generation Sequencing allows to identify transcripts containing both coding and non-coding RNAs, but no strategies have been identified so far to discover ncRNA (non-coding RNA) biological functions; thus, most of the ncRNA functionalities are still unknown. We propose a new approach to detect putative lncRNAs transcripts starting from an RNA-seq analysis performed by a reference-based assembly. The extracted transcripts are then analyzed to filter out protein transcripts, detecting putative, thus interesting, lncRNAs submitted to biologists for further validations.

The analysis of bioimages and their correlated clinical patient information allows to investigate specific diseases and define the corresponding medical protocols. To perform a correct diagnosis and apply a precise therapy, bioimages must be collected and studied together with others relevant data as well as laboratory results, medical annotations and patient history. Today, the management of these data is performed by single systems inside hospital departments that often do not provide dedicated data integration platforms among different departments as well as different health structures to exchange of relevant clinical information. Also, images cannot be annotated or enriched by physicians to trace temporal studies for patients or even among patients with similar diseases. In this contribution, we report the results of a research project called GIDAC (standing for Gestione Integrata DAti Clinici) that aims to define a general purpose framework for the bioimages management and annotations as well as clinical data view and integration in a simple-to-use information system. The proposed framework does not substitute any existing clinical information system but is able in gathering and integrating data by using a XML-based module. The novelty also consists in allowing annotations on DICOM images by means of simple user-interface to take trace of changes intra images as well as comparisons among patients. This system supports oncologists in the management of DICOM images from different devices (e.g., ecograph or PACS) to extract relevant information necessary to query (annotate) images and study similar clinical cases.

Quantitative analysis of PET images is a useful as well as essential practice to perform an objective measurement of a physiological process. It allows to study diseases, evaluating treatment response and comparing patients data by quantify images. The analysis consists in estimating the quantity of radionuclide tracer uptaken by tissues. We focus on quantitative analysis of dynamic PET studies to evaluate the diseases of coronary artery and myocardium perfusion. We report experiences on quantitative cardiac PET analysis by using a commercial and largely used software to evaluate viable myocardium through Patlak method. We report also results obtained on PET images provided by clinical departments of the Magna Graecia University Medical School of Catanzaro.

Biological networks are usually used to model interactions among biological macromolecules in a cells. For instance protein-protein interaction networks (PIN) are used to model and analyse the set of interactions among proteins. The comparison of networks may result in the identification of conserved patterns of interactions corresponding to biological relevant entities such as protein complexes and pathways. Several algorithms, known as network alignment algorithms, have been proposed to unravel relations between different species at the interactome level. Algorithms may be categorized in two main classes: merge and mine and mine and merge. Algorithms belonging to the first class initially merge input network into a single integrated and then mine such networks. Conversely algorithms belonging to the second class initially analyze separately two input networks then integrate such results. In this paper we present MODULA (Network Module based PPI Aligner), a novel approach for local network alignment that belong to the second class. The algorithm at first identifies compact modules from input networks. Modules of both networks are then matched using functional knowledge. Then it uses high scoring pairs of modules as seeds to build a bigger alignment. In order to asses MODULA we compared it to the state of the art local alignment algorithms over a rather extensive and updated dataset.

Health promotion represents the process to empower the citizens to improve their health lifestyle and to achieve higher levels of wellness. The health models focus on helping people to prevent illnesses through their behavior, and on looking at ways in which a person can pursue better health or ideal health. We report on a project aiming to propose a new model for wellness improvement, consisting in actions to be performed to encourage individuals to become aware of their wellness and develop healthier habits.

DNA is a long polymer being famous for its doublehelix form at the lower level, and for its chromosome packaging at higher levels of detail. Nonetheless DNA exists in many possible conformations, including A-DNA, B-DNA and Z-DNA forms. B-DNA is the most common form found in cells. Non-B DNAs comprise of tetraplex (G-quadruplex), left-handed Z-DNA, and others. Several recent publications have provided significant evidence that non-B DNA structures may play a role in DNA instability and mutagenesis, leading to both DNA rearrangements and increased mutational rates, which are a hallmark of cancer related diseases. Studying the structure conformation and probability of non-B DNA structure, may help in studying diseases as well as designing of new drugs. Nevertheless, even if there are some examples of prediction tools, the topic of designing efficient prediction algorithms and tools for G-quadruplex prediction is still in its infancy.As a contribution in this new area, we present preliminary results and statistics obtained by using the state of the art software tools able to predict G-quadruplex DNA conformations starting from the primary sequence. We used existing tools as well as known structures to define the state of the art and the current value of prediction tools. We believe that our study may represent an important contribution through the definition of reliable Gquadruplex prediction tools.

BACKGROUND: Protein inter-residue contact maps provide a translation and rotation invariant topological representation of a protein. They can be used as an intermediary step in protein structure predictions. However, the prediction of contact maps represents an unbalanced problem as far fewer examples of contacts than non-contacts exist in a protein structure.In this study we explore the possibility of completely eliminating the unbalanced nature of the contact map prediction problem by predicting real-value distances between residues. Predicting full inter-residue distance maps and applying them in protein structure predictions has been relatively unexplored in the past. RESULTS: We initially demonstrate that the use of native-like distance maps is able to reproduce 3D structures almost identical to the targets, giving an average RMSD of 0.5Å. In addition, the corrupted physical maps with an introduced random error of ±6Å are able to reconstruct the targets within an average RMSD of 2Å.After demonstrating the reconstruction potential of distance maps, we develop two classes of predictors using two-dimensional recursive neural networks: an ab initio predictor that relies only on the protein sequence and evolutionary information, and a template-based predictor in which additional structural homology information is provided. We find that the ab initio predictor is able to reproduce distances with an RMSD of 6Å, regardless of the evolutionary content provided. Furthermore, we show that the template-based predictor exploits both sequence and structure information even in cases of dubious homology and outperforms the best template hit with a clear margin of up to 3.7Å.Lastly, we demonstrate the ability of the two predictors to reconstruct the CASP9 targets shorter than 200 residues producing the results similar to the state of the machine learning art approach implemented in the Distill server. CONCLUSIONS: The methodology presented here, if complemented by more complex reconstruction protocols, can represent a possible path to improve machine learning algorithms for 3D protein structure prediction. Moreover, it can be used as an intermediary step in protein structure predictions either on its own or complemented by NMR restraints.

Open Data refers to the possibility of freely sharing data among users and organization. Similarly Open Government Initiatives refer to the sharing of documents and data among public governments and citizens. Here we focus on an open initiative held by the Italian Ministry of Health who is making available through Internet a set of Open Data about drug stores, health centers, and other health-related data. In particular, we propose a Cloud-based software tool able to gather and integrate different datasets made available by the Italian Ministry of Health. The proposed Cloud-based tool, called Open Health Data for Tourist (OHT), is able to offer to the tourist information about nearest health care providers (drug stores, public emergency room, hospitals and medical doctors) in Italy through an application accessible from mobile devices.

General-purpose Electronic Patient Records (EPR) may lack in support of specialistic clinical data that characterizes each clinical domain. In many clinical settings often such data remain inside the computer systems associated with specialistic instruments and are not readily available to clinicians for conducting large studies for research purposes. In this paper we present audioEPR, a specialized EPR for the semi-automatic management and querying of audiological and otoneurological clinical data. The goal of the tool is to support the day-by-day clinical activity as well as the simple selection and extraction of clinical data for research activity. The realized prototype is able to support the semi-automatic storage of data related to different Audiology tests and the generation of related diagnostic reports. To encourage its use in a clinical setting its interface resembles the form of the paper-based documents currently used by operators, while maintaining the formal correctness of the produced documentation. On the other hand, its database allows an easy selection and extraction of set of clinical data for research purposes.

Protein-protein interaction (PPI) databases store the whole set of protein interactions in organism. In spite of the availability of much biological information spread on different sources (e.g. Gene Ontology), neither proteins nor interactions are generally annotated in PPI databases. This results in very poor querying capabilities of PPI databases that enable very simple queries. The annotation of proteins and interactions stored in PPI databases may allow the implementation of more powerful querying interfaces. The paper presents a software architecture for the annotation of existing PPI databases with information extracted from Gene Ontology. A simple extension of the query interface of an existent PPI database is discussed.

Handling microarray data is particularly challenging mainly due to the high dimensionality of such data, which demands for computer-aided methods, and to the intrinsic difficulty of devising notions of proximity between spots of array traps. In this paper, we propose a new approach to modeling the probe-level uncertainty in microarray data that allows for a more expressive representation of the data and a more accurate processing. This approach is essentially based on a recently proposed method for uncertain data clustering. This method lies in a centroid-linkage-based agglomerative hierarchical algorithm, named U-AHC, and an information-theoretic-based distance measure between uncertain data . We have conducted experiments on four large microarray datasets, in order to assess effectiveness of the proposed clustering method. Experimental results have shown high quality results in terms of compactness of the clustering solutions.

Analyzing physiological brain responses to external stimuli helps neuroscientists to elucidate human behaviour and, more generally improves knowledge of neurological patients profile. It is well known that functional magnetic resonance imaging (fMRI) can provide important information when stimulating sensorimotor or cognitive functions in humans. In this paper we present a software tool supporting analysis of fMRI datasets. The tool allows medical operators to build sequences of stimuli that are presented to subjects within the MRI scanner and to define critical task parameters and timings. Patients feedbacks are recorded through a fiber-optic computer-controlled MR compatible system during the fMRI acquisition phase. The proposed tool, called StiMaRe (for Stimuli definition and analysis in Magnetic Resonance), includes a database layer allowing to store the defined pattern with the patient feedbacks. An XML based framework allows to distribute the pattern stimuli and results to remote sites, allowing the reissuing of the experiment on different samples.

Interactomics is the study of the Interactome, i.e. the whole set of macromolecular interactions within a cell. Proteins interact among them and different interactions are represented as graphs named Protein to Protein Interaction (PPI) networks. The interest in analyzing PPI networks is related to the possibility of predicting PPI properties on the basis of global properties of the graph (e.g. verify if homology among species involves PPI similarity), or to find set of protein interactions that has a biological meaning. The prediction of protein complexes has been faced in the last years by using different clustering algorithms. The Markov Clustering algorithm (MCL) is a method that presents one of the best performance but is currently available only as a stand alone application with a simple command-line interface available only on Linux platforms. Following a trend in bioinformatics, we provide a web portal (myMCL) allowing remote users to access MCL functions through the Internet. myMCL enables user to submit a job and stores results in a local database for further processing.

Preprocessing mass spectrometry (MS) data has been recognized as a crucial preliminary phase in order to perform data management and knowledge discovery tasks on mass spectra. The huge dimensionality and heterogeneity of MS data make mandatory the use of tools that are able to guide the user in the MS preprocessing task. However, most MS preprocessing tools are typically designed to perform only some preprocessing steps and are strictly coupled with MS data analysis modules. In this paper, we present mass spectra preprocessing tool (MSPtool), a user-friendly versatile tool for preprocessing MS data. MSPtool provides the user with a wide set of MS preprocessing steps by means of an easy-to-use graphical interface. Also, this tool has been embedded in a time-series-based framework for MS data clustering.

The simulation of the dynamics of the blood flow in the venous system of the lower limb is an important tool for supporting clinical research and for suggesting possible treatments for many diseases, e.g. for enhancing the surgical treatment of chronic venous insufficiency (CVI). Nevertheless the accuracy of the simulation of the blood flow is strictly related to the morphological data characterizing the investigated venous system. Although some of these data can be extracted from the observation of the real blood flow of a patient, e.g. through the acquisition of a set of images, the extraction of such values is often performed in a manual way, so the need for the automatic induction of parameters arises. The paper presents a software module that allows the semiautomatic acquisition of the morphological data of the venous system of a patient. The tool, developed as a plugin of the ImageJ imaging platform, receives in input a DICOM file containing the computerized tomography (CT) of the vessels network of the lower limb, and produces in a semi-automatic way a weighted graph of the network. This model can be used as the input for a subsequent simulation of the system.

Mass spectrometry (MS) is a technique that is used for biological studies. It consists in associating a spectrum to a biological sample. A spectrum consists of couples of values (intensity, m/z), where intensity measures the abundance of biomolecules (as proteins) with a mass-to-charge ratio (m/z) present in the originating sample. In proteomics experiments, MS spectra are used to identify pattern expressions in clinical samples that may be responsible of diseases. Recently, to improve the identification of peptides/proteins related to patterns, MS/MS process is used, consisting in performing cascade of mass spectrometric analysis on selected peaks. Latter technique has been demonstrated to improve the identification and quantification of proteins/peptide in samples. Nevertheless, MS analysis deals with a huge amount of data, often affected by noises, thus requiring automatic data management systems. Tools have been developed and most of the time furnished with the instruments allowing: (i) spectra analysis and visualization, (ii) pattern recognition, (iii) protein databases querying, (iv) peptides/proteins quantification and identification. Currently most of the tools supporting such phases need to be optimized to improve the protein (and their functionalities) identification processes. In this article we survey on applications supporting spectrometrists and biologists in obtaining information from biological samples, analyzing available software for different phases. We consider different mass spectrometry techniques, and thus different requirements. We focus on tools for (i) data preprocessing, allowing to prepare results obtained from spectrometers to be analyzed; (ii) spectra analysis, representation and mining, aimed to identify common and/or hidden patterns in spectra sets or in classifying data; (iii) databases querying to identify peptides; and (iv) improving and boosting the identification and quantification of selected peaks. We trace some open problems and report on requirements that represent new challenges for bioinformatics.

This paper presents a methodology to mine spectra data based on time-series analysis. MALDI-TOF spectra are modelled as time series using a compact yet feature-rich representation scheme. Experiments show that classifying mass spectrometry series is effective and can be useful for identifying peaks in spectra that can be associated to discriminant proteins.

BACKGROUND: Isotope-coded affinity tags (ICAT) is a method for quantitative proteomics based on differential isotopic labeling, sample digestion and mass spectrometry (MS). The method allows the identification and relative quantification of proteins present in two samples and consists of the following phases. First, cysteine residues are either labeled using the ICAT Light or ICAT Heavy reagent (having identical chemical properties but different masses). Then, after whole sample digestion, the labeled peptides are captured selectively using the biotin tag contained in both ICAT reagents. Finally, the simplified peptide mixture is analyzed by nanoscale liquid chromatography-tandem mass spectrometry (LC-MS/MS). Nevertheless, the ICAT LC-MS/MS method still suffers from insufficient sample-to-sample reproducibility on peptide identification. In particular, the number and the type of peptides identified in different experiments can vary considerably and, thus, the statistical (comparative) analysis of sample sets is very challenging. Low information overlap at the peptide and, consequently, at the protein level, is very detrimental in situations where the number of samples to be analyzed is high. RESULTS: We designed a method for improving the data processing and peptide identification in sample sets subjected to ICAT labeling and LC-MS/MS analysis, based on cross validating MS/MS results. Such a method has been implemented in a tool, called EIPeptiDi, which boosts the ICAT data analysis software improving peptide identification throughout the input data set. Heavy/Light (H/L) pairs quantified but not identified by the MS/MS routine, are assigned to peptide sequences identified in other samples, by using similarity criteria based on chromatographic retention time and Heavy/Light mass attributes. EIPeptiDi significantly improves the number of identified peptides per sample, proving that the proposed method has a considerable impact on the protein identification process and, consequently, on the amount of potentially critical information in clinical studies. The EIPeptiDi tool is available at http://bioingegneria.unicz.it/~veltri/projects/eipeptidi/ with a demo data set. CONCLUSION: EIPeptiDi significantly increases the number of peptides identified and quantified in analyzed samples, thus reducing the number of unassigned H/L pairs and allowing a better comparative analysis of sample data sets.

Abstract Mass spectrometry proteomics data contain much information about cell functions and disease conditions. The discovery of such information is enabled by the combined use of novel bioinformatics tools and data mining techniques requiring the integration of huge data sources and the composition of different software tools. The main phases of such emerging applications comprise the loading, management, preprocessing, mining, and visualization of spectra, as well as the analysis of discovered knowledge models. The collection, storage, and analysis of spectra produced in different laboratories can make use of the services of computational Grids, which offer efficient data transfer primitives, effective management of large data stores, and large computing power. In this paper we present MS‐Analyzer, a Grid‐based software platform for the integrated management and analysis of spectra data. MS‐Analyzer provides efficient spectra management through a specialized spectra database, and supports the semantic composition of spectra preprocessing services and data mining services to analyze spectra on the Grid. Copyright © 2006 John Wiley & Sons, Ltd.

This paper presents a methodology for analyzing and classifying proteins identified in biological samples. In particular, such methodology consists in normalizing and classifying quantity and quality of proteins identified by using tandem mass spectrometry. A case study is considered and a classification experiment for protein discriminant is also reported

Identifying protein secondary structures is a difficult task. Recently, a lot of software tools for protein secondary structure prediction have been produced and made available on-line, mostly with good performances. However, prediction tools work correctly for families of proteins, such that users have to know which predictor to use for a given unknown protein. We propose a framework to improve secondary structure prediction by integrating results obtained from a set of available predictors. Our contribution consists in the definition of a two phase approach: (i) select a set of predictors which have good performances with the unknown protein family, and (U) integrate the prediction results of the selected prediction tools. Experimental results are also reported

The combined use of mass spectrometry and data mining is a novel approach in proteomic pattern analysis for discovering novel biomarkers or identifying patterns and associations in proteomic profiles. Data produced by mass spectrometers are affected by errors and noise due to sample preparation and instrument approximation, so different preprocessing techniques need to be applied before analysis is conducted. We survey different techniques for spectra preprocessing, and we present a first design of a software tool that allows the preprocessing, management and analysis of mass spectrometry data on the Grid.

Although originally designed for large-scale electronic publishing, XML plays an increasingly important role in the exchange of data on the Web. In fact, it is expected that XML will become the lingua franca of the Web, eventually replacing HTML. Not surprisingly, there has been a great deal of interest on XML both in industry and in academia. Nevertheless, to date no comprehensive study on the XML Web (i.e., the subset of the Web made of XML documents only) nor on its contents has been made. This paper is the first attempt at describing the XML Web and the documents contained in it. Our results are drawn from a sample of a repository of the publicly available XML documents on the Web, consisting of about 200,000 documents. Our results show that, despite its short history, XML already permeates the Web, both in terms of generic domains and geographically. Also, our results about the contents of the XML Web provide valuable input for the design of algorithms, tools and systems that use XML in one form or another.