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      Self-Organizing Feature Maps Identify Proteins Critical to Learning in a Mouse Model of Down Syndrome

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      PLoS ONE
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          Abstract

          Down syndrome (DS) is a chromosomal abnormality (trisomy of human chromosome 21) associated with intellectual disability and affecting approximately one in 1000 live births worldwide. The overexpression of genes encoded by the extra copy of a normal chromosome in DS is believed to be sufficient to perturb normal pathways and normal responses to stimulation, causing learning and memory deficits. In this work, we have designed a strategy based on the unsupervised clustering method, Self Organizing Maps (SOM), to identify biologically important differences in protein levels in mice exposed to context fear conditioning (CFC). We analyzed expression levels of 77 proteins obtained from normal genotype control mice and from their trisomic littermates (Ts65Dn) both with and without treatment with the drug memantine. Control mice learn successfully while the trisomic mice fail, unless they are first treated with the drug, which rescues their learning ability. The SOM approach identified reduced subsets of proteins predicted to make the most critical contributions to normal learning, to failed learning and rescued learning, and provides a visual representation of the data that allows the user to extract patterns that may underlie novel biological responses to the different kinds of learning and the response to memantine. Results suggest that the application of SOM to new experimental data sets of complex protein profiles can be used to identify common critical protein responses, which in turn may aid in identifying potentially more effective drug targets.

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            The KEGG database.

            KEGG (http://www.genome.ad.jp/kegg/) is a suite of databases and associated software for understanding and simulating higher-order functional behaviours of the cell or the organism from its genome information. First, KEGG computerizes data and knowledge on protein interaction networks (PATHWAY database) and chemical reactions (LIGAND database) that are responsible for various cellular processes. Second, KEGG attempts to reconstruct protein interaction networks for all organisms whose genomes are completely sequenced (GENES and SSDB databases). Third, KEGG can be utilized as reference knowledge for functional genomics (EXPRESSION database) and proteomics (BRITE database) experiments. I will review the current status of KEGG and report on new developments in graph representation and graph computations.
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              Machine learning in bioinformatics.

              This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization. Applications in genomics, proteomics, systems biology, evolution and text mining are also shown.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                25 June 2015
                2015
                : 10
                : 6
                : e0129126
                Affiliations
                [1 ]Departamento de Bioquímica y Biología Molecular I, Facultad de Ciencias Químicas, Universidad Complutense, Madrid, Spain
                [2 ]Departamento de Inteligencia Artificial e Ingeniería del Software, Facultad de Informática, Universidad Complutense, Madrid, Spain
                [3 ]Linda Crnic Institute for Down Syndrome, Department of Pediatrics, Department of Biochemistry and Molecular Genetics, Human Medical Genetics and Genomics, and Neuroscience Programs, University of Colorado, School of Medicine, Aurora, Colorado, United States of America
                [4 ]Department of Computer Science, Virginia Commonwealth University, Richmond, Virginia, United States of America
                [5 ]IITiS, Polish Academy of Sciences, Gliwice, Poland
                IGBMC/ICS, FRANCE
                Author notes

                Competing Interests: The authors declare that no competing interests exist.

                Conceived and designed the experiments: CH KG KC. Performed the experiments: KG CH. Analyzed the data: CH KG KC. Contributed reagents/materials/analysis tools: KG CH. Wrote the paper: CH KG KC.

                Article
                PONE-D-14-41629
                10.1371/journal.pone.0129126
                4482027
                26111164
                0698f1e1-c550-43e3-95d3-58f8230f7025
                Copyright @ 2015

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                : 17 September 2014
                : 5 May 2015
                Page count
                Figures: 9, Tables: 6, Pages: 28
                Funding
                This study was supported by the National Institutes of Health ( http://www.nih.gov/); grant number 1R01HD056235-01A1 (KG, KC); Ministerio de Economía y Competitividad ( http://www.mineco.gob.es/portal/site/mineco/); grant number BFU2012-39816-C02-02 (CH). CH holds a FPI PhD scholarship from Ministerio de Economía y Competitividad (Spain). The funding agencies had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Custom metadata
                All relevant data are available via Figshare ( http://dx.doi.org/10.6084/m9.figshare.1421985).

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