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      GENIES: gene network inference engine based on supervised analysis

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          Abstract

          Gene network inference engine based on supervised analysis (GENIES) is a web server to predict unknown part of gene network from various types of genome-wide data in the framework of supervised network inference. The originality of GENIES lies in the construction of a predictive model using partially known network information and in the integration of heterogeneous data with kernel methods. The GENIES server accepts any ‘profiles’ of genes or proteins (e.g. gene expression profiles, protein subcellular localization profiles and phylogenetic profiles) or pre-calculated gene–gene similarity matrices (or ‘kernels’) in the tab-delimited file format. As a training data set to learn a predictive model, the users can choose either known molecular network information in the KEGG PATHWAY database or their own gene network data. The user can also select an algorithm of supervised network inference, choose various parameters in the method, and control the weights of heterogeneous data integration. The server provides the list of newly predicted gene pairs, maps the predicted gene pairs onto the associated pathway diagrams in KEGG PATHWAY and indicates candidate genes for missing enzymes in organism-specific metabolic pathways. GENIES ( http://www.genome.jp/tools/genies/) is publicly available as one of the genome analysis tools in GenomeNet.

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          A combined algorithm for genome-wide prediction of protein function.

          The availability of over 20 fully sequenced genomes has driven the development of new methods to find protein function and interactions. Here we group proteins by correlated evolution, correlated messenger RNA expression patterns and patterns of domain fusion to determine functional relationships among the 6,217 proteins of the yeast Saccharomyces cerevisiae. Using these methods, we discover over 93,000 pairwise links between functionally related yeast proteins. Links between characterized and uncharacterized proteins allow a general function to be assigned to more than half of the 2,557 previously uncharacterized yeast proteins. Examples of functional links are given for a protein family of previously unknown function, a protein whose human homologues are implicated in colon cancer and the yeast prion Sup35.
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            Kernel methods for predicting protein-protein interactions.

            Despite advances in high-throughput methods for discovering protein-protein interactions, the interaction networks of even well-studied model organisms are sketchy at best, highlighting the continued need for computational methods to help direct experimentalists in the search for novel interactions. We present a kernel method for predicting protein-protein interactions using a combination of data sources, including protein sequences, Gene Ontology annotations, local properties of the network, and homologous interactions in other species. Whereas protein kernels proposed in the literature provide a similarity between single proteins, prediction of interactions requires a kernel between pairs of proteins. We propose a pairwise kernel that converts a kernel between single proteins into a kernel between pairs of proteins, and we illustrate the kernel's effectiveness in conjunction with a support vector machine classifier. Furthermore, we obtain improved performance by combining several sequence-based kernels based on k-mer frequency, motif and domain content and by further augmenting the pairwise sequence kernel with features that are based on other sources of data. We apply our method to predict physical interactions in yeast using data from the BIND database. At a false positive rate of 1% the classifier retrieves close to 80% of a set of trusted interactions. We thus demonstrate the ability of our method to make accurate predictions despite the sizeable fraction of false positives that are known to exist in interaction databases. The classification experiments were performed using PyML available at http://pyml.sourceforge.net. Data are available at: http://noble.gs.washington.edu/proj/sppi.
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              PathPred: an enzyme-catalyzed metabolic pathway prediction server

              The KEGG RPAIR database is a collection of biochemical structure transformation patterns, called RDM patterns, and chemical structure alignments of substrate-product pairs (reactant pairs) in all known enzyme-catalyzed reactions taken from the Enzyme Nomenclature and the KEGG PATHWAY database. Here, we present PathPred (http://www.genome.jp/tools/pathpred/), a web-based server to predict plausible pathways of muti-step reactions starting from a query compound, based on the local RDM pattern match and the global chemical structure alignment against the reactant pair library. In this server, we focus on predicting pathways for microbial biodegradation of environmental compounds and biosynthesis of plant secondary metabolites, which correspond to characteristic RDM patterns in 947 and 1397 reactant pairs, respectively. The server provides transformed compounds and reference transformation patterns in each predicted reaction, and displays all predicted multi-step reaction pathways in a tree-shaped graph.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                July 2012
                July 2012
                14 June 2012
                14 June 2012
                : 40
                : Web Server issue
                : W162-W167
                Affiliations
                1Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan and 2Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
                Author notes
                *To whom correspondence should be addressed. Tel: +81 774 38 3271; Fax: +81 774 38 3269; Email: goto@ 123456kuicr.kyoto-u.ac.jp

                The authors wish it to be known that, in their opinion, the first three authors should be regarded as joint First Authors.

                Article
                gks459
                10.1093/nar/gks459
                3394336
                22610856
                55130b4c-23b3-4c6e-ba5d-e1dece70f4cb
                © The Author(s) 2012. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 11 February 2012
                : 24 April 2012
                : 30 April 2012
                Page count
                Pages: 6
                Categories
                Articles

                Genetics
                Genetics

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