IgRENE is a web-based platform designed to integrate gene regulatory networks (GRNs) with drug-based regulatory data through a comprehensive, criteria-driven computational framework. The proposed tool provides a methodology for analyzing and identifying drugs along with their regulatory pathways, combining both upstream and downstream effects to assess the modulation of gene expression. This approach aids in prioritizing potential therapeutic compounds for more efficient further experimental validation and clinical testing. By consolidating gene-to-gene and drug-to-gene regulatory interactions within a unified interface, IgRENE provides researchers in drug discovery, systems biology, and precision medicine with a powerful tool that highlights drug candidates predicted to have a desired downstream effect on the expression of disease associated genes.

Prioritization or ranking of different cell types in a single-cell RNA sequencing (scRNA-seq) framework can be performed in a variety of ways, some of these include: i) obtaining an indication of the proportion of cell types between the different conditions under study, ii) counting the number of differentially expressed genes (DEGs) between cell types and conditions in the experiment or, iii) prioritizing cell types based on prior knowledge about the conditions under study (i.e., a specific disease). These methods have drawbacks and limitations thus novel methods for improving cell ranking are required. Here we present a novel methodology that exploits prior knowledge in combination with expert-user information to accentuate cell types from a scRNA-seq analysis that yield the most biologically meaningful results with respect to a disease under study. Our methodology allows for ranking and prioritization of cell types based on how well their expression profiles relate to the molecular mechanisms and drugs associated with a disease. Molecular mechanisms, as well as drugs, are incorporated as prior knowledge in a standardized, structured manner. Cell types are then ranked/prioritized based on how well results from data-driven analysis of scRNA-seq data match the predefined prior knowledge. In additional cell-cell communication perturbations between disease and control networks are used to further prioritize/rank cell types. Our methodology has substantial advantages to more traditional cell ranking techniques and provides an informative complementary methodology that utilizes prior knowledge in a rapid and automated manner, that has previously not been attempted by other studies. The current methodology is also implemented as an R package entitled Single Cell Ranking Analysis Toolkit (scRANK) and is available for download and installation via GitHub (https://github.com/aoulas/scRANK).

We draw from the assumption that similarities between pathogens at both pathogen protein and host protein level, may provide the appropriate framework to identify and rank candidate drugs to be used against a specific pathogen. Vir2Drug is a drug repurposing tool that uses network-based approaches to identify and rank candidate drugs for a specific pathogen, combining information obtained from: (a) ranked pathogen-to-pathogen networks based on protein similarities between pathogens, (b) taxonomy distance between pathogens and (c) drugs targeting specific pathogen's and host proteins. The underlying pathogen networks are used to screen drugs by means of specific methodologies that account for either the host or pathogen's protein targets. Vir2Drug is a useful and yet informative tool for drug repurposing against known or unknown pathogens especially in periods where the emergence for repurposed drugs plays significant role in handling viral outbreaks, until reaching a vaccine. The web tool is available at: https://bioinformatics.cing.ac.cy/vir2drug, https://vir2drug.cing-big.hpcf.cyi.ac.cy.

During the course of a viral infection, virus-host protein-protein interactions (PPIs) play a critical role in allowing viruses to replicate and survive within the host. These interspecies molecular interactions can lead to viral-mediated perturbations of the human interactome causing the generation of various complex diseases. Evidences suggest that viral-mediated perturbations are a possible pathogenic etiology in several neurodegenerative diseases (NDs). These diseases are characterized by chronic progressive degeneration of neurons, and current therapeutic approaches provide only mild symptomatic relief; therefore, there is unmet need for the discovery of novel therapeutic interventions. In this paper, we initially review databases and tools that can be utilized to investigate viral-mediated perturbations in complex NDs using network-based analysis by examining the interaction between the ND-related PPI disease networks and the virus-host PPI network. Afterwards, we present our theoretical-driven integrative network-based bioinformatics approach that accounts for pathogen-genes-disease-related PPIs with the aim to identify viral-mediated pathogenic mechanisms focusing in multiple sclerosis (MS) disease. We identified seven high centrality nodes that can act as disease communicator nodes and exert systemic effects in the MS-enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways network. In addition, we identified 12 KEGG pathways, 5 Reactome pathways and 52 Gene Ontology Immune System Processes by which 80 viral proteins from eight viral species might exert viral-mediated pathogenic mechanisms in MS. Finally, our analysis highlighted the Th17 differentiation pathway, a disease communicator node and part of the 12 underlined KEGG pathways, as a key viral-mediated pathogenic mechanism and a possible therapeutic target for MS disease.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is undeniably the most severe global health emergency since the 1918 Influenza outbreak. Depending on its evolutionary trajectory, the virus is expected to establish itself as an endemic infectious respiratory disease exhibiting seasonal flare-ups. Therefore, despite the unprecedented rally to reach a vaccine that can offer widespread immunization, it is equally important to reach effective prevention and treatment regimens for coronavirus disease 2019 (COVID-19). Contributing to this effort, we have curated and analyzed multi-source and multi-omics publicly available data from patients, cell lines and databases in order to fuel a multiplex computational drug repurposing approach. We devised a network-based integration of multi-omic data to prioritize the most important genes related to COVID-19 and subsequently re-rank the identified candidate drugs. Our approach resulted in a highly informed integrated drug shortlist by combining structural diversity filtering along with experts' curation and drug-target mapping on the depicted molecular pathways. In addition to the recently proposed drugs that are already generating promising results such as dexamethasone and remdesivir, our list includes inhibitors of Src tyrosine kinase (bosutinib, dasatinib, cytarabine and saracatinib), which appear to be involved in multiple COVID-19 pathophysiological mechanisms. In addition, we highlight specific immunomodulators and anti-inflammatory drugs like dactolisib and methotrexate and inhibitors of histone deacetylase like hydroquinone and vorinostat with potential beneficial effects in their mechanisms of action. Overall, this multiplex drug repurposing approach, developed and utilized herein specifically for SARS-CoV-2, can offer a rapid mapping and drug prioritization against any pathogen-related disease.

BACKGROUND: Next-generation sequencing (NGS) represents a significant advancement in clinical genetics. However, its use creates several technical, data interpretation and management challenges. It is essential to follow a consistent data analysis pipeline to achieve the highest possible accuracy and avoid false variant calls. Herein, we aimed to compare the performance of twenty-eight combinations of NGS data analysis pipeline compartments, including short-read mapping (BWA-MEM, Bowtie2, Stampy), variant calling (GATK-HaplotypeCaller, GATK-UnifiedGenotyper, SAMtools) and interval padding (null, 50 bp, 100 bp) methods, along with a commercially available pipeline (BWA Enrichment, Illumina®). Fourteen germline DNA samples from breast cancer patients were sequenced using a targeted NGS panel approach and subjected to data analysis. RESULTS: We highlight that interval padding is required for the accurate detection of intronic variants including spliceogenic pathogenic variants (PVs). In addition, using nearly default parameters, the BWA Enrichment algorithm, failed to detect these spliceogenic PVs and a missense PV in the TP53 gene. We also recommend the BWA-MEM algorithm for sequence alignment, whereas variant calling should be performed using a combination of variant calling algorithms; GATK-HaplotypeCaller and SAMtools for the accurate detection of insertions/deletions and GATK-UnifiedGenotyper for the efficient detection of single nucleotide variant calls. CONCLUSIONS: These findings have important implications towards the identification of clinically actionable variants through panel testing in a clinical laboratory setting, when dedicated bioinformatics personnel might not always be available. The results also reveal the necessity of improving the existing tools and/or at the same time developing new pipelines to generate more reliable and more consistent data.

SUMMARY: ChemBioServer 2.0 is the advanced sequel of a web server for filtering, clustering and networking of chemical compound libraries facilitating both drug discovery and repurposing. It provides researchers the ability to (i) browse and visualize compounds along with their physicochemical and toxicity properties, (ii) perform property-based filtering of compounds, (iii) explore compound libraries for lead optimization based on perfect match substructure search, (iv) re-rank virtual screening results to achieve selectivity for a protein of interest against different protein members of the same family, selecting only those compounds that score high for the protein of interest, (v) perform clustering among the compounds based on their physicochemical properties providing representative compounds for each cluster, (vi) construct and visualize a structural similarity network of compounds providing a set of network analysis metrics, (vii) combine a given set of compounds with a reference set of compounds into a single structural similarity network providing the opportunity to infer drug repurposing due to transitivity, (viii) remove compounds from a network based on their similarity with unwanted substances (e.g. failed drugs) and (ix) build custom compound mining pipelines. AVAILABILITY AND IMPLEMENTATION: http://chembioserver.vi-seem.eu.

MOTIVATION: Understanding the underlying biological mechanisms and respective interactions of a disease remains an elusive, time consuming and costly task. Computational methodologies that propose pathway/mechanism communities and reveal respective relationships can be of great value as they can help expedite the process of identifying how perturbations in a single pathway can affect other pathways. RESULTS: We present a random-walks-based methodology called PathWalks, where a walker crosses a pathway-to-pathway network under the guidance of a disease-related map. The latter is a gene network that we construct by integrating multi-source information regarding a specific disease. The most frequent trajectories highlight communities of pathways that are expected to be strongly related to the disease under study.We apply the PathWalks methodology on Alzheimer's disease and idiopathic pulmonary fibrosis and establish that it can highlight pathways that are also identified by other pathway analysis tools as well as are backed through bibliographic references. More importantly, PathWalks produces additional new pathways that are functionally connected with those already established, giving insight for further experimentation. AVAILABILITY AND IMPLEMENTATION: https://github.com/vagkaratzas/PathWalks. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Huntington's disease (HD), a rare autosomal dominant disease, affecting the medium spiny neurons of the CNS. Although HD is caused by a trinucleotide repeat in the HTT gene, it is a complex disease. Systems Bioinformatics which combines systems biology and bioinformatics, has the ability to reveal synergistic relationships between multiple entities. This approach is vital as it can shed light on the biological behavior and mechanisms of the cell rather than only trying to study and understand a part of the system. Metabolomics is the systematic study and measurement of metabolites within a biological sample. In this work, we employ two approaches to identify metabolites for HD-related pathways, which were previously identified from our previous work on multi-source data integration These include: i) creation of pathway-to-pathway networks based on the reference network of PathwayConnector where pathways are mapped based on connectivity on KEGG, and (ii) creation of pathway-to-pathway networks using a pairwise approach, where a connection between two pathways exists only if they share common metabolites.

Spastic ataxia (SA) is a term used to describe a neurodegenerative disorder that is characterised by imbalance and incoordination in gait and limbs, accompanied by spasticity. Recently the GBA2 gene has been reported as an SA associated gene. In 2014, Votsi et al., reported a novel missense mutation (Asp594His) in a Cypriot consanguineous family with SA. However, the pathogenetic mechanism that leads to the development of this disease remains unclear. A multisource-data integration approach developed by our team, was used for the prediction of candidate pathways that may be involved in GBA2-related diseases, including SA. Protein-protein interactions and partnerships are the basis for understanding molecular pathways. Changes in the interaction sites of GBA2 may disrupt allosterically its catalytic activity, related pathway regulation and contribute towards the development of SA. In this work, protein-protein interactions of the wild-type and mutant GBA2 were predicted using a sequence-based predictor tool, called SPRINT, in order to identify loss or gain of protein-protein interactions of the mutant protein. Finally, we performed pathway analysis of the lost or gained protein interactors and the candidate pathways resulting from this study were compared with the ones predicted by our previous multi-source integration approach. Our method aims to discover candidate pathways that may be affected and enhance our understanding of the pathogenetic mechanisms that may underlie the development of SA.

Fibrotic diseases constitute incurable maladies that affect a large portion of the population. Idiopathic Pulmonary Fibrosis is one of the most common, and thus studied, fibrotic diseases. Common ground among all fibrotic diseases is the uncontrollable fibrogenesis which is responsible for accumulated damage in the susceptible tissues. The plethora and complexity of the underlying mechanisms of fibrotic diseases account for the lack of regimens. Hence it is highly likely that a combination of drugs is required in order to counter every perturbation. In this study, we seek to identify common biological mechanisms and characteristics of fibrotic diseases, based on information acquired from biological databases, while we focus on Idiopathic Pulmonary Fibrosis. We also try to predict links between molecular data and their respective fibrotic phenotypes. We finally construct phenotypic and molecular networks, visualize them and apply a clustering algorithm on each network to identify fibrotic diseases that are close to Idiopathic Pulmonary Fibrosis.

The assessment of solitary pulmonary nodules (SPNs) is a diagnostic task that requires precision. We have investigated alternative ways of SPN classification working on both Positron Emission Tomography (PET) and Computed Tomography (CT) images as well as on re-calculated sinograms. The best classification schemes have been included in a graphical interface accompanied with risk estimators based on clinical and other data. This is a forerunner for an SPN Computer Aided Diagnosis (CAD) system expected to be a precious tool in the hands of medical doctors.

The evolution of Next Generation Sequencing (NGS) technologies represents a significant advancement in the field of molecular genetics and has set the ground, for the discovery of novel variants which cannot be easily classified as deleterious or neutral. In-vitro and in-vivo characterization of these variants of uncertain clinical significance (VUS) should be followed; however, it is often not feasible to carry out the experimental interpretation for every single VUS. In silico tools have been crucial for the prediction of the impact of VUS on protein structure, stability and function. Our aim was to combine computational approaches to investigate the impact of VUS identified in a cohort of Cypriot Triple-Negative Breast Cancer (TNBC) patients by NGS. Using a combination of structural, functional and network-based bioinformatics approaches for the classification of a nonsense PRF1 mutation in association with BC susceptibility, we propose a possible triggered interaction of the mutant PRF1 protein with the CDKN2A protein, a product of a BC susceptibility gene. Additionally, our results support that the increased probability of interaction of the mutant counterpart of perforin with its top 10 predicted interactors, could play an important role in the obstruction of cellular processes related to carcinogenesis such as cell death, necrosis, DNA damage, immortality, UV stress, DNA repair and cell cycle control. We conclude that probably the nonsense PRF1 mutation could be associated with BC predisposition. However, although in silico tools provide an important tool for the interpretation of VUS, functional studies, co-segregation analyses and/or case-control association studies are needed to draw conclusions on variant classification.

Almost 2500 years after Hippocrates' observations on health and its direct association to the gastrointestinal tract, a paradigm shift has recently occurred, making the gut and its symbionts (bacteria, fungi, archaea and viruses) a point of convergence for studies. It is nowadays well established that the gut microflora's compositional diversity regulates via its genes (the microbiome) the host's health and provides preliminary insights into disease progression and regulation. The microbiome's involvement is evident in immunological and physiological studies that link changes in its biodiversity to its contributions to the host's phenotype but also in neurological investigations, substantiating the aptly named gut-brain axis. The definitive mechanisms of this last bidirectional interaction will be our main focus because it presents researchers with a new conundrum. In this review, we prospect current literature for computational analysis methodologies that accommodate the need for better understanding of the microbiome-gut-brain interactions and neurological disorder onset and progression, through cross-disciplinary systems biology applications. We will present bioinformatics tools used in exploring these synergies that help build and interpret microbial 16S ribosomal RNA data sets, produced by shotgun and high-throughput sequencing of healthy and neurological disorder samples stored in biological databases. These approaches provide alternative means for researchers to form hypotheses to their inquests faster, cheaper and swith precision. The goal of these studies relies on the integration of combined metagenomics and metabolomics assessments. An accurate characterization of the microbiome and its functionality can support new diagnostic, prognostic and therapeutic strategies for neurological disorders, customized for each individual host.

Systems Bioinformatics is a relatively new approach, which lies in the intersection of systems biology and classical bioinformatics. It focuses on integrating information across different levels using a bottom-up approach as in systems biology with a data-driven top-down approach as in bioinformatics. The advent of omics technologies has provided the stepping-stone for the emergence of Systems Bioinformatics. These technologies provide a spectrum of information ranging from genomics, transcriptomics and proteomics to epigenomics, pharmacogenomics, metagenomics and metabolomics. Systems Bioinformatics is the framework in which systems approaches are applied to such data, setting the level of resolution as well as the boundary of the system of interest and studying the emerging properties of the system as a whole rather than the sum of the properties derived from the system's individual components. A key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers. Here, we provide evidence on how Systems Bioinformatics enhances computational therapeutics and diagnostics, hence paving the way to precision medicine. The aim of this review is to familiarize the reader with the emerging field of Systems Bioinformatics and to provide a comprehensive overview of its current state-of-the-art methods and technologies. Moreover, we provide examples of success stories and case studies that utilize such methods and tools to significantly advance research in the fields of systems biology and systems medicine.

SUMMARY: PathwayConnector is a web-tool that facilitates the construction of complementary pathway-to-pathway networks and subnetworks of them, based on a reference pathway network derived from the rich information available either in KEGG or Reactome database for pathway mapping. Specifically, for a given set of pathways, PathwayConnector (i) finds all the direct connections between them, (ii) adds a minimum set of complementary pathways required to achieve connectivity between the pathways, leading to informative fully connected networks and (ii) provides a series of clustering methods for the further grouping of pathways in to sub-clusters. The proposed web-tool is a simple yet informative tool towards identifying connected groups of pathways that are significantly related to specific diseases. AVAILABILITY AND IMPLEMENTATION: http://bioinformatics.cing.ac.cy/PathwayConnector. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The advancement of scientific and medical research over the past years has generated a wealth of experimental data from multiple technologies, including genomics, transcriptomics, proteomics, and other forms of -omics data, which are available for a number of diseases. The integration of such multisource data is a key component toward the success of precision medicine. In this paper, we are investigating a multisource data integration method developed by our group, regarding its ability to drive to clusters of connected pathways under two different approaches: first, a disease-centric approach, where we integrate data around a disease, and second, a gene-centric approach, where we integrate data around a gene. We have used as a paradigm for the first approach Huntington's disease (HD), a disease with a plethora of available data, whereas for the second approach the GBA2, a gene that is related to spastic ataxia (SA), a phenotype with sparse availability of data. Our paper shows that valuable information at the level of disease-related pathway clusters can be obtained for both HD and SA. New pathways that classical pathway analysis methods were unable to reveal, emerged as necessary "connectors" to build connected pathway stories formed as pathway clusters. The capability to integrate multisource molecular data, concluding to something more than the sum of the existing information, empowers precision and personalized medicine approaches.

Differential rewiring of cellular interaction networks between disease and healthy state is of great importance. Through a systems level approach, malfunctioned mechanisms that are absent in the normal cases, may enlighten the key-players in terms of genes and their interaction chains related to disease. We have developed D-Map, a publicly available user-friendly web application, capable of generating and manipulating advanced differential networks by combining state-of-the-art inference reconstruction methods with random walk simulations. The inputs are expression profiles obtained from the Gene Expression Omnibus and a gene list under investigation. Differential networks may be visualized and interpreted through the use of D-Map interface, where display of the disease, the normal and the common state can be performed, interactively. A case study scenario concerning Alzheimer's disease, as well as breast, lung, and bladder cancer was conducted in order to demonstrate the usefulness of the proposed methodology to different disease types. Findings were consistent with the current bibliography, and the provided interaction lists may be further explored towards novel biological insights of the investigated diseases. The DMap web-application is available at: http://bioserver-3.bioacademy.gr/Bioserver/DMap/index.php.

Alarming epidemiological features of Alzheimer's disease impose curative treatment rather than symptomatic relief. Drug repurposing, that is reappraisal of a substance's indications against other diseases, offers time, cost and efficiency benefits in drug development, especially when in silico techniques are used. In this study, we have used gene signatures, where up- and down-regulated gene lists summarize a cell's gene expression perturbation from a drug or disease. To cope with the inherent biological and computational noise, we used an integrative approach on five disease-related microarray data sets of hippocampal origin with three different methods of evaluating differential gene expression and four drug repurposing tools. We found a list of 27 potential anti-Alzheimer agents that were additionally processed with regard to molecular similarity, pathway/ontology enrichment and network analysis. Protein kinase C, histone deacetylase, glycogen synthase kinase 3 and arginase inhibitors appear consistently in the resultant drug list and may exert their pharmacologic action in an epidermal growth factor receptor-mediated subpathway of Alzheimer's disease.

BACKGROUND: So far many algorithms have been proposed towards the detection of significant genes in microarray analysis problems. Several of those approaches are freely available as R-packages though their engagement in gene expression analysis by non-bioinformaticians is usually a frustrating task. Besides, only some of those packages offer a complete suite of tools starting from initial data import and ending to analysis report. Here we present an R/Bioconductor package that implements a hybrid gene selection method along with a bunch of functions to facilitate a thorough and convenient gene expression profiling analysis. RESULTS: mAPKL is an open-source R/Bioconductor package that implements the mAP-KL hybrid gene selection method. The advantage of this method is that selects a small number of gene exemplars while achieving comparable classification results to other well established algorithms on a variety of datasets and dataset sizes. The mAPKL package is accompanied with extra functionalities including (i) solid data import; (ii) data sampling following a user-defined proportion; (iii) preprocessing through several normalization and transformation alternatives; (iv) classification with the aid of SVM and performance evaluation; (v) network analysis of the significant genes (exemplars), including degree of centrality, closeness, betweeness, clustering coefficient as well as the construction of an edge list table; (vi) gene annotation analysis, (vii) pathway analysis and (viii) auto-generated analysis reporting. CONCLUSIONS: Users are able to run a thorough gene expression analysis in a timely manner starting from raw data and concluding to network characteristics of the selected gene exemplars. Detailed instructions and example data are provided in the R package, which is freely available at Bioconductor under the GPL-2 or later license http://www.bioconductor.org/packages/3.1/bioc/html/mAPKL.html.

We have developed ZoomOut web server in order to provide the research community with a tool for analysis, visualization and clustering of networks as a super network, based on their calculated feature properties. Networks can be analysed and be further treated as single nodes in a super network that describe their relations. Specifically, the user interface is divided into three main sections: the Workspace, the Networks Feature Calculations and the Clustering Networks section. In the Workspace section, users are able to upload and manage multiple networks for further processing. In the Networks Feature Calculations section, a variety of network properties are calculated as features for each uploaded network. In the Clustering Networks section, users are able to apply clustering by selecting from the list of previously calculated features. All processed networks can also be visualized as a super interactive network, were interconnections among networks are based on the calculated clustering distances. To the best of our knowledge, this is the first available web-service that allows users to manage, quantify and visualize multiple networks at the same time, handling them as parts of a larger network with properties calculated in an upper scale. The ZoomOut web-application is available at http://bioserver-3.bioacademy.gr/Bioserver/ZoomOut.

A computer-aided diagnosis (CADx ) framework for the diagnosis of clustered microcalcifications (MCs) has already been developed, which is based on the analysis of MCs' morphologies,the shape of the cluster they form and the texture of the surrounding tissue. In this study, we investigate the diagnostic information that the relative location of the cluster inside the breast may provide. Breast probabilistic maps are generated and adopted in the CADx pipeline, expecting to empower its diagnostic procedure. We propose a flowchart combining alternative classification algorithms and the aforementioned probabilistic maps in order to provide a final risk for malignancy for new considered mammograms. For the evaluation performance, a large dataset of mammograms provided from the Digital Database of Screening Mammography (DDSM) has been used. The obtained results indicate that the proposed modifications lead to the enhancement of the diagnostic process, as the classification results are further improved. Additionally, a straightforward comparison between the CADx pipeline and the radiologists who assessed the same mammograms, reveal that the CADx pipeline performs toward the right direction, as the sensitivity remains at high levels, while improving both the accuracy, from 51.4% to 69%, and the specificity, from 16.6% to 54.7%.

Breast microcalcifications are one of the most important mammographic findings related to the existence of the breast cancer. Radiologists usually characterize microcalcifications based on their morphologies, the distribution within the cluster they form, the shape of the cluster and its relative location inside the breast. In this study, we focus on the latter factor and we study its effect on the probability of malignancy. The main purpose of our study is to generate probabilistic breast cancer atlases for clusters of microcalcifications in order to visualize the influence of cluster location on cancer probability. We propose a framework for the generation of such atlases, including segmentation of important breast landmarks and projection of different clusters of microcalcifications on a reference breast shape. The generation of the atlases is implemented using mammograms from the Digital Database of Screening Mammography. The obtained probabilistic atlases reveal specific areas in the breast of higher occurrence of clusters and higher risk of malignancy.

In this work, we study the factors that influence the efficacy of a proposed Computer Aided Diagnosis (CADx) framework for the diagnosis of clustered microcalcifications (MCs) using a large dataset of mammograms containing cases of varying breast density and findings' subtlety. The reported results indicate that the proposed framework performs towards the right direction, as it appears high classification performance (Az=0.909) for specific subsets of cases, while outperforming at the same time the performance of the radiologists who evaluated the same cases. The effect of the initial enhancement of mammograms in the CADxpipeline is then investigated, by applying three different image enhancement techniques on several subsets of mammograms. We observed that for the considered subsets of dense mammograms, a wavelet-based enhancement algorithm outperformed the rest and provided superior classification performance (Az=0.849). We indicate therefore that the density of the breast determines the need of different computational algorithms for the analysis of a mammogram and as a result the a priori knowledge of this factor may be exploited for the optimization of the diagnostic process.

Breast cancer can be prevented with regular mammography screening. Yet, the incorporation of Computational Intelligence relies on training classifiers on a set of predefined Regions of Interest (ROIs). Data Clustering has been applied to address the problem of ROI detection, yet no extensive research has been carried out on which algorithm to utilize. This contribution focuses on microcalcification clustering as a Data Clustering application, giving insights concerning the performance of three main clustering algorithms.

The aim of this study was the development of a simulator for Multispectral Optoacoustic Tomography (MSOT). The modelling pathway of the simulator was separated into the optical, the acoustic and the reconstruction part in generating finally a photoacoustic image. In this paper, the presented simulation geometry was based on a recently developed MSOT imaging system, but it can be easily modified to other imaging geometries. Through comparison between experimental and simulated data, a validation of the model as well as its limitations, perspectives and modifications are presented.

The assessment of solitary pulmonary nodules (SPN) is a very difficult task in PET imaging due to adjacent normal structures. Physicians and computational systems as well, would gain benefit if trained to a large number of SPN cases with controlled topological and morphological characteristics. Our objective was to develop a method for the modeling of the solitary pulmonary nodules in CT and PET images. The modeling of SPN was implemented by Monte Carlo methods taking into consideration morphological characteristics, internal features and Standardized Uptake Value (SUV) activity distribution. For the validation of the model, an observer study from three independent medical experts was performed. The reviewers characterized the lesions as simulated or real and finally they classified them as benign or malignant. According to the results of the human observer study a significant percentage of simulated images could not be differentiated from real ones and the simulated class (benign or malignant) was consistent with the observers' classification.

Breast cancer diagnosis requires specific expertise from the Medical Doctors. Furthermore, prognosis, monitoring and early detection of malignant findings can be successfully realized through synergies between physicians, researchers and general population in concert with the health policy program of each country. Information technology and computational intelligence play a crucial role in the production of digital repositories and cancer registries as well as in the development of systems to support diagnosis. In this paper we present the concept and the architecture of an approved grant under the National Strategic Reference Framework 2007-2013 (NSRF), called e-Prolipsis. Through this project, we will design and implement a web based risk estimation platform and a Central Breast Cancer Registry (CBCR) that will co-operate to provide medical doctors and patients with services such as multiplicity in diagnostic opinions, synergy, computational risk estimation, access to statistical/epidemiological data for trend estimation. Such a system, will serve as a reference tool and will help the clinician, the radiologist, the rural doctor or trainee doctor in the final assessment on the existence or likelihood of breast cancer. The system will assist in the successful diagnosis of breast cancer, giving each patient access to a large pool of doctors and at the same time, the data stored in the CBCR will be used for statistical analysis, providing useful results for improving both the cancer detection application and for making a national policy for combating breast cancer. This system will be accessible through a Web Portal, with different access levels for patients, doctors and general public and different web services available to each user group, eliminating the geographical distances that would be imposed otherwise.

The application of an in-house developed web server called ChemBioServer to the filtering and selection of drug candidates for the inhibition of the PI3Kα protein is presented. 1000 candidate molecules were initially selected from a virtual screening experiment. Those molecules were then filtered for steric clashes, physicochemical and toxicity properties and grouped into clusters using the ChemBioServer web application. During this filtering process, 400 compounds were rejected and the remaining 600 were clustered in 20 different groups, allowing for a more efficient visual inspection of the compounds. Representatives of these clusters were then selected for further experimental study. Four out of the seven selected molecules inhibited PI3Kα activity in vitro, indicating that the workflow described herein can be successfully applied in drug discovery. ChemBioServer proved to assist the post-processing application of top-ranked molecules resulting from a docking exercise by increasing the efficiency and the quality of compound selection that passed to the experimental test phase.

This paper studies the application of fuzzy clustering with viewpoints in order to cluster cell samples according to their gene expression profile. This method combines fuzzy clustering with external domain knowledge represented by the so-called viewpoints. The viewpoints that we employ are obtained from previously available expression data. The method was compared to the clustering algorithms of k-means, fuzzy c-means, affinity propagation, as well as a method of clustering microarray data that is based on prior biological knowledge, and has shown comparable/improved results over them.

SUMMARY: ChemBioServer is a publicly available web application for effectively mining and filtering chemical compounds used in drug discovery. It provides researchers with the ability to (i) browse and visualize compounds along with their properties, (ii) filter chemical compounds for a variety of properties such as steric clashes and toxicity, (iii) apply perfect match substructure search, (iv) cluster compounds according to their physicochemical properties providing representative compounds for each cluster, (v) build custom compound mining pipelines and (vi) quantify through property graphs the top ranking compounds in drug discovery procedures. ChemBioServer allows for pre-processing of compounds prior to an in silico screen, as well as for post-processing of top-ranked molecules resulting from a docking exercise with the aim to increase the efficiency and the quality of compound selection that will pass to the experimental test phase. AVAILABILITY: The ChemBioServer web application is available at: http://bioserver-3.bioacademy.gr/Bioserver/ChemBioServer/. CONTACT: [email protected]

BACKGROUND: A feature selection method in microarray gene expression data should be independent of platform, disease and dataset size. Our hypothesis is that among the statistically significant ranked genes in a gene list, there should be clusters of genes that share similar biological functions related to the investigated disease. Thus, instead of keeping N top ranked genes, it would be more appropriate to define and keep a number of gene cluster exemplars. RESULTS: We propose a hybrid FS method (mAP-KL), which combines multiple hypothesis testing and affinity propagation (AP)-clustering algorithm along with the Krzanowski & Lai cluster quality index, to select a small yet informative subset of genes. We applied mAP-KL on real microarray data, as well as on simulated data, and compared its performance against 13 other feature selection approaches. Across a variety of diseases and number of samples, mAP-KL presents competitive classification results, particularly in neuromuscular diseases, where its overall AUC score was 0.91. Furthermore, mAP-KL generates concise yet biologically relevant and informative N-gene expression signatures, which can serve as a valuable tool for diagnostic and prognostic purposes, as well as a source of potential disease biomarkers in a broad range of diseases. CONCLUSIONS: mAP-KL is a data-driven and classifier-independent hybrid feature selection method, which applies to any disease classification problem based on microarray data, regardless of the available samples. Combining multiple hypothesis testing and AP leads to subsets of genes, which classify unknown samples from both, small and large patient cohorts with high accuracy.

The discovery of potential microarray markers, which will expedite molecular diagnosis/prognosis and provide reliable results to clinical decision-making and treatment selection for patients, is of paramount importance. Feature selection techniques, which aim at minimizing the dimensionality of the microarray data by keeping the most statistically significant genes, are a powerful approach toward this goal. In this paper, we investigate the minimum required subsets of genes, which best classify neuromuscular disease data. For this purpose, we implemented a methodology pipeline that facilitated the use of multiple feature selection methods and subsequent performance of data classification. Five feature selection methods on datasets from ten different neuromuscular diseases were utilized. Our findings reveal subsets of very small number of genes, which can successfully classify normal/disease samples. Interestingly, we observe that similar classification results may be obtained from different subsets of genes. The proposed methodology can expedite the identification of small gene subsets with high-classification accuracy that could ultimately be used in the genetics clinics for diagnostic, prognostic, and pharmacogenomic purposes.

UNLABELLED: The uniqueness of a measured molecular mass or peptide sequence plays a very important role in the fields of protein identification and peptide/protein-biomarker investigation. We present a publicly available web application that offers information concerning the uniqueness of one or more molecular masses and one or more peptide sequences in the human proteome. When a sequence is found to be unique in humans, the application is able to search across all species querying whether this sequence is unique, not only in humans but also in other species found in the Swiss-Prot Database. The application is also able to search for unique protein fragments derived computationally from enzymatic digestion driven by certain enzymes. Furthermore, the application can list all the unique masses and peptides of a given protein. Through this application, researchers are able to find unique tags, either on a molecular mass level or on a sequence level. These unique tags are remarkably important in research related to protein identification or biomarker discovery and measurements. AVAILABILITY: UniMaP web-application is available at http://bioserver-1.bioacademy.gr/Bioserver/UniMaP/

Since modern Biology has been transformed to a quantitative science dealing with tremendous amounts of data, the applications of information science and technology on Biology, i.e. Bioinformatics, are so many that they comprise a separate dataset needing organization and indexing for optimum search and information retrieval. Metabasis, a web-based database system for organizing and maintaining information relevant to published bioinformatics tools and databases has been developed by our group. However, such an effort requires rapid and massive information update, an issue quite difficult since the bioinformatics tools production rate is very high and there is no standard protocol in the way they are presented in the literature. We present here a high throughput automatic retrieval procedure to keep alive a database of this kind, using certain methodology for automatic downloading, parsing and filtering data from the related literature.

Fold recognition based on sequence-derived features is a complex classification problem and usually sequence-derived features are exploited using proper machine learning techniques. Here we adress the task of fold recognition on a protein similarity network (PSN) basis. We construct a protein sequence similarity network (PSeSN) using a set of 125 sequence-derived features for an available set of 311 proteins. PSeSN is optimized by using a Genetic Algorithm (GA) to select the features that construct a PSeSN which is as similar as possible with the corresponding protein structure similarity network (PStSN). A random walk based algorithm is then utilized to recognize the fold of a query protein sequence by calculating its affinities to sequences-vertices both in the initial and the optimized PSeSN. Total accuracy (TA) measurements obtained using 10-fold cross validation show that the use of 48 out of 125 sequence-derived features (optimized PSeSN) yielded better results (mean TA: 0.35 in testing sets) than the initial PSeSN (mean TA: 0.316 in testing sets).

UNLABELLED: The identification of unknown amino acid sequences of peptides as well as protein identification is of great significance in proteomics. Here, we present a publicly available web application that facilitates a high resolution mapping of measured molecular masses to peptides and proteins, irrespectively of the enzyme/digestion method used. Furthermore, multi-filtering may be applied in terms of measured mass tolerance, molecular mass and isoelectric point range as well as pattern matching to refine the results. This approach serves complementary to the existing solutions for protein identification and gives insights in novel peptides discovery and protein identification at the cases where the identification scores from the other approaches may be below significance threshold. Peptide Finder has been proven useful in proteomics procedures with experimental data from MALDI-TOF. AVAILABILITY: Peptide Finder web-application is available at http://bioserver-1.bioacademy.gr/Bioserver/PeptideFinder/.