Alfredo Pulvirenti

Program Committee Chair Mourad Elloumi, UTIC, University of Tunis, Tunisia ... Program Committee El Ghazali Talbi, University of Lille, INRIA, CNRS, France Albert Y. Zomaya, The University of Sydney, Australia Nadia Pisanti, University of Pisa, Italy Mohammed Sohel Rahman, King's College London, United Kingdom Raffaele Giancarlo, University of Palermo, Italy Giorgio Valentini, University of Milano, Italy Alexandros Stamatakis, University of Munich, Germany Mohamed Elati, University of Evry Val-d'Essonne, France Matteo Comin, University of Padova, Italy Alfredo ...

Mourad Elloumi, LaTICE, University of Tunis, Tunisia (PC Chair) Costas S. Iliopoulos, King's College London, UK Jason TL Wang, New Jersey Institute of Technology, USA Albert Y. Zomaya, University of Sydney, Australia Mohammed S. Rahman, King's College London, UK Daisuke Kihara, Purdue University, West Lafayette, IN, USA Alfredo Pulvirenti, University of Catania, Italy José Luis Oliveira, University of Aveiro, Portugal Carlo Cattani, University of Salerno, Italy Shoba Ranganathan, University, Sydney, Australia Radha Krishna Murthy ...

Cirrhotic patients after liver transplantation show a near-normal glucose homeostasis when in stable condition. In contrast, the basal and insulin-mediated whole-body protein metabolism remain altered several years after the graft. To examine whether the persisting defect of protein metabolism was due to the muscle, 7 non-diabetic liver-transplanted patients in stable condition were studied by means of the catheterization of the brachial artery and the deep forearm vein (to measure the balance across the forearm) and the infusion of labelled leucine and phenylalanine associated with indirect calorimetry. Whole-body proteolysis (as determined by endogenous leucine flux, ELF), protein synthesis (from non-oxidative leucine disposal, NOLD) and leucine oxidation (LO) were reduced in comparison to previously obtained values in a normal population. Insulin infusion (while maintaining euglycemia) induced a not significant variation of forearm phenylalanine Ra (24.4-->16.5 micromol/100 ml forearm min(-1); proteolysis) and Rd (18.5-->19.7; protein synthesis). In contrast, the whole-body insulin-dependent inhibitions of ELF (31.5-->21.8 micromol/m(2) min) and NOLD (27.3-->18.4) were impaired with respect to a normal population. On the basis of the present results, we conclude that skeletal muscle is not responsible for the alterations of leucine metabolism persisting after liver transplantation. By exclusion, this points to the liver as the major determinant of the leucine metabolism defect.

Biological network querying is a problem requiring a considerable computational effort to be solved. Given a target and a query network, it aims to find occurrences of the query in the target by considering topological and node similarities (i.e. mismatches between nodes, edges, or node labels). Querying tools that deal with similarities are crucial in biological network analysis because they provide meaningful results also in case of noisy data. In addition, as the size of available networks increases steadily, existing algorithms and tools are becoming unsuitable. This is rising new challenges for the design of more efficient and accurate solutions. This paper presents APPAGATO, a stochastic and parallel algorithm to find approximate occurrences of a query network in biological networks. APPAGATO handles node, edge and node label mismatches. Thanks to its randomic and parallel nature, it applies to large networks and, compared with existing tools, it provides higher performance as well as statistically significant more accurate results. Tests have been performed on protein-protein interaction networks annotated with synthetic and real gene ontology terms. Case studies have been done by querying protein complexes among different species and tissues. APPAGATO has been developed on top of CUDA-C ++ Toolkit 7.0 framework. The software is available online http://profs.sci.univr.it/∼bombieri/APPAGATO CONTACT: rosalba.giugno@univr.itSupplementary information: Supplementary data are available at Bioinformatics online.

Prediction of phenotypes from high-dimensional data is a crucial task in precision biology and medicine. Many technologies employ genomic biomarkers to characterize phenotypes. However, such elements are not sufficient to explain the underlying biology. To improve this, pathway analysis techniques have been proposed. Nevertheless, such methods have shown lack of accuracy in phenotypes classification. Here we propose a novel methodology called MITHrIL (Mirna enrIched paTHway Impact anaLysis) for the analysis of signaling pathways, which extends the work of Tarca et al., 2009. MITHrIL augments pathways with missing regulatory elements, such as microRNAs, and their interactions with genes. The method takes as input the expression values of genes and/or microRNAs and returns a list of pathways sorted according to their degree of deregulation, together with the corresponding statistical significance (p-values). Our analysis shows that MITHrIL outperforms its competitors even in the worst case. In addition, our method is able to correctly classify sets of tumor samples drawn from TCGA. MITHrIL is freely available at the following URL: http://alpha.dmi.unict.it/mithril/.

Rapid developments in science and engineering are producing a profound effect on the way information is represented. A new problem in pattern recognition has emerged: new data forms such as trees representing XML documents and images cannot been treated efficiently by classical storing and searching methods. In this paper we improve trie-based data structures by adding data mining techniques to

To evaluate the demographic, geographical, and race-related variables that account for geographical variability in prevalence rates of Age-related macular degeneration (AMD). Systematic review, metaregression, and decision-tree analysis. A systematic literature review of PubMed, Medline, Web of Science, and Embase databases identified population-based studies on the prevalence of AMD published before May 2014. Only population-based studies that took place in a spatially explicit geographic area that could be geolocalised, and used retinal photographs and standardised grading classifications were included. Latitude and longitude data (geolocalisation) and the mean annual insolation for the area where survey took place were obtained. Age-standardised prevalence rates across studies were estimated using the direct standardisation method. Correlations between the prevalence of AMD and longitude and latitude were obtained by regression analysis. A hierarchical Bayesian meta-regression ap...

We present NetMatchStar, a Cytoscape app to find all the occurrences of a query graph in a network and check for its significance as a motif with respect to seven different random models. The query can be uploaded or built from scratch using Cytoscape facilities. The app significantly enhances the previous NetMatch in style, performance and functionality. Notably NetMatchStar allows queries with wildcards.

Obsessive-compulsive disorder (OCD) is a psychiatric condition characterized by intrusive and unwilling thoughts (obsessions) giving rise to anxiety. The patients feel obliged to perform a behavior (compulsions) induced by the obsessions. The World Health Organization ranks OCD as one of the 10 most disabling medical conditions. In the class of Anxiety Disorders, OCD is a pathology that shows an hereditary component. Consequently, an online resource collecting and integrating scientific discoveries and genetic evidence about OCD would be helpful to improve the current knowledge on this disorder. We have developed a manually curated database, OCD Database (OCDB), collecting the relations between candidate genes in OCD, microRNAs (miRNAs) involved in the pathophysiology of OCD and drugs used in its treatments. We have screened articles from PubMed and MEDLINE. For each gene, the bibliographic references with a brief description of the gene and the experimental conditions are shown. The database also lists the polymorphisms within genes and its chromosomal regions. OCDB data is enriched with both validated and predicted miRNA-target and drug-target information. The transcription factors regulations, which are also included, are taken from David and TransmiR. Moreover, a scoring function ranks the relevance of data in the OCDB context. The database is also integrated with the main online resources (PubMed, Entrez-gene, HGNC, dbSNP, DrugBank, miRBase, PubChem, Kegg, Disease-ontology and ChEBI). The web interface has been developed using phpMyAdmin and Bootstrap software. This allows (i) to browse data by category and (ii) to navigate in the database by searching genes, miRNAs, drugs, SNPs, regions, drug targets and articles. The data can be exported in textual format as well as the whole database in.sql or tabular format. OCDB is an essential resource to support genome-wide analysis, genetic and pharmacological studies. It also facilitates the evaluation of genetic data in OCD and the detection of alternative treatments.

Vitis vinifera (Grapevine) is the most important fruit species in the modern world. Wine and table grapes sales contribute significantly to the economy of major wine producing countries. The most relevant goals in wine production concern quality and safety. In order to significantly improve the achievement of these objectives and to gain biological knowledge about cultivars, a genomic approach is the most reliable strategy. The recent grapevine genome sequencing offers the opportunity to study the potential roles of genes and microRNAs in fruit maturation and other physiological and pathological processes. Although several systems allowing the analysis of plant genomes have been reported, none of them has been designed specifically for the functional analysis of grapevine genomes of cultivars under environmental stress in connection with microRNA data. Here we introduce a novel knowledge base, called BIOWINE, designed for the functional analysis of Vitis vinifera genomes of cultivar...

Protein-protein interaction (PPI) networks available in public repositories usually represent relationships between proteins within the cell. They ignore the specific set of tissues or tumors where the interactions take place. Indeed, proteins can form tissue-selective complexes, while they remain inactive in other tissues. For these reasons, a great attention has been recently paid to tissue-specific PPI networks, in which nodes are proteins of the global PPI network whose corresponding genes are preferentially expressed in specific tissues. In this paper, we present SPECTRA, a knowledge base to build and compare tissue or tumor-specific PPI networks. SPECTRA integrates gene expression and protein interaction data from the most authoritative online repositories. We also provide tools for visualizing and comparing such networks, in order to identify the expression and interaction changes of proteins across tissues, or between the normal and pathological states of the same tissue. SPECTRA is available as a web server at http://alpha.dmi.unict.it/spectra.

Celiac disease (CD) is one of the commonest lifelong disorders in countries populated by individuals of European origin, affecting approximately 1% of the general population. This is a common disease also in North Africa, Middle East and India. The widespread diffusion of CD is not surprising given that its causal factors (HLA predisposing genotypes and consumption of gluten-containing cereals) show a worldwide distribution. Further studies are needed to quantify the incidence of CD in apparently "celiac-free" areas such as Sub-Saharan Africa and Far East. Several reports have shown that CD is increasing in frequency in different geographic areas. Genetic factors do not explain the rising incidence during the last decades; environmental or lifestyle factors may be responsible for these changes over time. The majority of patients with CD are still undiagnosed all over the world, leading to debate about the need of screening program.

Rapid developments in science and engineering are producing a profound effect on the way information is represented. A new problem in pattern recognition has emerged: new data forms such as trees representing XML documents and images cannot been treated efficiently by classical storing and searching methods. In this paper we improve trie-based data structures by adding data mining techniques to

Multiple local structure comparison helps to identify common structural motifs or conserved binding sites in 3D structures in distantly related proteins. Since there is no best way to compare structures and evaluate the alignment, a wide variety of techniques and different similarity scoring schemes have been proposed. Existing algorithms usually compute the best superposition of two structures or attempt to solve it as an optimization problem in a simpler setting (e.g., considering contact maps or distance matrices). Here, we present PROPOSAL (PROteins comparison through Probabilistic Optimal Structure local ALignment), a stochastic algorithm based on iterative sampling for multiple local alignment of protein structures. Our method can efficiently find conserved motifs across a set of protein structures. Only the distances between all pairs of residues in the structures are computed. To show the accuracy and the effectiveness of PROPOSAL we tested it on a few families of protein structures. We also compared PROPOSAL with two state-of-the-art tools for pairwise local alignment on a dataset of manually annotated motifs. PROPOSAL is available as a Java 2D standalone application or a command line program at http://ferrolab.dmi.unict.it/proposal/proposal.html.

Research attention has been powered to understand the functional roles of non-coding RNAs (ncRNAs). Many studies have demonstrated their deregulation in cancer and other human disorders. ncRNAs are also present in extracellular human body fluids such as serum and plasma, giving them a great potential as non-invasive biomarkers. However, non-coding RNAs have been relatively recently discovered and a comprehensive database including all of them is still missing. Reconstructing and visualizing the network of ncRNAs interactions are important steps to understand their regulatory mechanism in complex systems. This work presents ncRNA-DB, a NoSQL database that integrates ncRNAs data interactions from a large number of well established on-line repositories. The interactions involve RNA, DNA, proteins, and diseases. ncRNA-DB is available at http://ncrnadb.scienze.univr.it/ncrnadb/. It is equipped with three interfaces: web based, command-line, and a Cytoscape app called ncINetView. By acces...