Literature DB >> 16671401

Ontology-driven approaches to analyzing data in functional genomics.

Francisco Azuaje1, Fatima Al-Shahrour, Joaquin Dopazo.   

Abstract

Ontologies are fundamental knowledge representations that provide not only standards for annotating and indexing biological information, but also the basis for implementing functional classification and interpretation models. This chapter discusses the application of gene ontology (GO) for predictive tasks in functional genomics. It focuses on the problem of analyzing functional patterns associated with gene products. This chapter is divided into two main parts. The first part overviews GO and its applications for the development of functional classification models. The second part presents two methods for the characterization of genomic information using GO. It discusses methods for measuring functional similarity of gene products, and a tool for supporting gene expression clustering analysis and validation.

Mesh:

Year:  2006        PMID: 16671401     DOI: 10.1385/1-59259-964-8:67

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  14 in total

1.  Mutation-prone points in thrombin receptor.

Authors:  Viroj Wiwanitkit
Journal:  J Thromb Thrombolysis       Date:  2007-12-07       Impact factor: 2.300

2.  Mutation-prone points in KCNQ.

Authors:  Viroj Wiwanitkit
Journal:  Exp Clin Cardiol       Date:  2008

3.  A weighted multipath measurement based on gene ontology for estimating gene products similarity.

Authors:  Lizhen Liu; Xuemin Dai; Hanshi Wang; Wei Song; Jingli Lu
Journal:  J Comput Biol       Date:  2014-12       Impact factor: 1.479

4.  Phenex: ontological annotation of phenotypic diversity.

Authors:  James P Balhoff; Wasila M Dahdul; Cartik R Kothari; Hilmar Lapp; John G Lundberg; Paula Mabee; Peter E Midford; Monte Westerfield; Todd J Vision
Journal:  PLoS One       Date:  2010-05-05       Impact factor: 3.240

5.  An improved method for scoring protein-protein interactions using semantic similarity within the gene ontology.

Authors:  Shobhit Jain; Gary D Bader
Journal:  BMC Bioinformatics       Date:  2010-11-15       Impact factor: 3.169

6.  GO-based functional dissimilarity of gene sets.

Authors:  Norberto Díaz-Díaz; Jesús S Aguilar-Ruiz
Journal:  BMC Bioinformatics       Date:  2011-09-01       Impact factor: 3.169

7.  Evaluation of GO-based functional similarity measures using S. cerevisiae protein interaction and expression profile data.

Authors:  Tao Xu; Linfang Du; Yan Zhou
Journal:  BMC Bioinformatics       Date:  2008-11-06       Impact factor: 3.169

8.  Functional assessment of time course microarray data.

Authors:  María José Nueda; Patricia Sebastián; Sonia Tarazona; Francisco García-García; Joaquín Dopazo; Alberto Ferrer; Ana Conesa
Journal:  BMC Bioinformatics       Date:  2009-06-16       Impact factor: 3.169

9.  Experimental-confirmation and functional-annotation of predicted proteins in the chicken genome.

Authors:  Teresia J Buza; Fiona M McCarthy; Shane C Burgess
Journal:  BMC Genomics       Date:  2007-11-19       Impact factor: 3.969

10.  Post hoc pattern matching: assigning significance to statistically defined expression patterns in single channel microarray data.

Authors:  Randall Hulshizer; Eric M Blalock
Journal:  BMC Bioinformatics       Date:  2007-07-05       Impact factor: 3.169

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