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A knowledge-based clustering algorithm driven by Gene Ontology.

Jill Cheng·Melissa Cline·John Martin·David Finkelstein·Tarif Awad·David Kulp·Michael A Siani-Rose
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Abstract

We have developed an algorithm for inferring the degree of similarity between genes by using the graph-based structure of Gene Ontology (GO). We applied this knowledge-based similarity metric to a clique-finding algorithm for detecting sets of related genes with biological classifications. We also combined it with an expression-based distance metric to produce a co-cluster analysis, which accentuates genes with both similar expression profiles and similar biological characteristics and identifies gene clusters that are more stable and biologically meaningful. These algorithms are demonstrated in the analysis of MPRO cell differentiation time series experiments.

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AlgorithmsArtificial IntelligenceCell DifferentiationCluster AnalysisHumansNeutrophilsOligonucleotide Array Sequence AnalysisTretinoinJournal Article

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Jill Cheng, Melissa Cline, John Martin, David Finkelstein, Tarif Awad, David Kulp, Michael A Siani-Rose. 2004. A knowledge-based clustering algorithm driven by Gene Ontology.. https://doi.org/10.1081/bip-200025659

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