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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

Protein structure prediction is a key topic in computational structural proteomics. The hypothesis that protein biological functions are implied by their three-dimensional structure makes the protein tertiary structure prediction a relevant problem to be solved. Predicting the tertiary structure of a protein by using its residue sequence is called the protein folding problem. Recently, novel approaches to the solution of this problem have been found and many of them use contact maps as a guide during the prediction process. Contact map structures are bidimensional objects which represent some of the structural information of a protein. Many approaches and bioinformatics tools for contact map prediction have been presented during the past years, having different performances for different protein families. In this work we present a novel approach based on the integration of contact map predictions in order to improve the quality of the predicted contact map with a consensus-based algorithm.

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.

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.

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.

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.