ArticleBMC genomics2007
GO-2D: identifying 2-dimensional cellular-localized functional modules in Gene Ontology.
Article in BMC genomics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Identification of differentially expressed genes by means of outlier detection.BMC bioinformatics · 2018Article
- Molecular signature of cancer at gene level or pathway level? Case studies of colorectal cancer and prostate cancer microarray data.Computational and mathematical methods in medicine · 2013Article
- Evaluating reproducibility of differential expression discoveries in microarray studies by considering correlated molecular changes.Bioinformatics (Oxford, England) · 2009Article
- VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology.Nucleic acids research · 2009Article
- Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists.Nucleic acids research · 2009Article
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8 authors.
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Abstract
backgroundRapid progress in high-throughput biotechnologies (e.g. microarrays) and exponential accumulation of gene functional knowledge make it promising for systematic understanding of complex human diseases at functional modules level. Based on Gene Ontology, a large number of automatic tools have been developed for the functional analysis and biological interpretation of the high-throughput microarray data.
resultsDifferent from the existing tools such as Onto-Express and FatiGO, we develop a tool named GO-2D for identifying 2-dimensional functional modules based on combined GO categories. For example, it refines biological process categories by sorting their genes into different cellular component categories, and then extracts those combined categories enriched with the interesting genes (e.g., the differentially expressed genes) for identifying the cellular-localized functional modules. Applications of GO-2D to the analyses of two human cancer datasets show that very specific disease-relevant processes can be identified by using cellular location information.
conclusionFor studying complex human diseases, GO-2D can extract functionally compact and detailed modules such as the cellular-localized ones, characterizing disease-relevant modules in terms of both biological processes and cellular locations. The application results clearly demonstrate that 2-dimensional approach complementary to current 1-dimensional approach is powerful for finding modules highly relevant to diseases.
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