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Volume 9 Supplement 1

Quantitative inference of gene function from diverse large-scale datasets

Research

Edited by Timothy R Hughes and Frederick P Roth

  1. Content type: Research

    Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help...

    Authors: Lourdes Peña-Castillo, Murat Tasan, Chad L Myers, Hyunju Lee, Trupti Joshi, Chao Zhang, Yuanfang Guan, Michele Leone, Andrea Pagnani, Wan Kyu Kim, Chase Krumpelman, Weidong Tian, Guillaume Obozinski, Yanjun Qi, Sara Mostafavi, Guan Ning Lin…

    Citation: Genome Biology 2008 9(Suppl 1):S2

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  2. Content type: Research

    The wide availability of genome-scale data for several organisms has stimulated interest in computational approaches to gene function prediction. Diverse machine learning methods have been applied to unicellul...

    Authors: Yuanfang Guan, Chad L Myers, David C Hess, Zafer Barutcuoglu, Amy A Caudy and Olga G Troyanskaya

    Citation: Genome Biology 2008 9(Suppl 1):S3

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  3. Content type: Method

    Most successful computational approaches for protein function prediction integrate multiple genomics and proteomics data sources to make inferences about the function of unknown proteins. The most accurate of ...

    Authors: Sara Mostafavi, Debajyoti Ray, David Warde-Farley, Chris Grouios and Quaid Morris

    Citation: Genome Biology 2008 9(Suppl 1):S4

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  4. Content type: Method

    The complete set of mouse genes, as with the set of human genes, is still largely uncharacterized, with many pieces of experimental evidence accumulating regarding the activities and expression of the genes, b...

    Authors: Wan Kyu Kim, Chase Krumpelman and Edward M Marcotte

    Citation: Genome Biology 2008 9(Suppl 1):S5

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  5. Content type: Method

    In predicting hierarchical protein function annotations, such as terms in the Gene Ontology (GO), the simplest approach makes predictions for each term independently. However, this approach has the unfortunate...

    Authors: Guillaume Obozinski, Gert Lanckriet, Charles Grant, Michael I Jordan and William Stafford Noble

    Citation: Genome Biology 2008 9(Suppl 1):S6

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  6. Content type: Method

    Learning the function of genes is a major goal of computational genomics. Methods for inferring gene function have typically fallen into two categories: 'guilt-by-profiling', which exploits correlation between...

    Authors: Weidong Tian, Lan V Zhang, Murat Taşan, Francis D Gibbons, Oliver D King, Julie Park, Zeba Wunderlich, J Michael Cherry and Frederick P Roth

    Citation: Genome Biology 2008 9(Suppl 1):S7

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  7. Content type: Research

    Individual researchers are struggling to keep up with the accelerating emergence of high-throughput biological data, and to extract information that relates to their specific questions. Integration of accumula...

    Authors: Murat Taşan, Weidong Tian, David P Hill, Francis D Gibbons, Judith A Blake and Frederick P Roth

    Citation: Genome Biology 2008 9(Suppl 1):S8

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