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Probabilistic framework for gene expression clustering validation based on gene ontology and graph theory
Based on the correlation between expression and ontology-driven gene similarity, we incorporate functional annotations into gene expression clustering validation. A probabilistic framework is proposed to accommodate incomplete annotations, after establishing a new term-term distance measure based on graph theory. Comprehensive evaluations are performed on six clustering algorithms. This study is the first to explore a robust quantitative functional relationship between clusters of genes. Such indices assess clustering quality in terms of consistency of annotation information and serve as new tools for combining biological knowledge with experimental data.
History
Event
IEEE Signal Processing Society. International Conference (2008 : Las Vegas, Nevada)Series
IEEE Signal Processing Society International ConferencePagination
625 - 628Publisher
Institute of Electrical and Electronics EngineersLocation
Las Vegas, NevadaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2008-03-30End date
2008-04-04ISSN
1520-6149ISBN-13
9781424414840ISBN-10
1424414849Language
engPublication classification
E1.1 Full written paper - refereedEditor/Contributor(s)
[Unknown]Title of proceedings
ICASSP 2008 : Proceedings of the 2008 IEEE International Conference on Acoustics, Speech and Signal ProcessingUsage metrics
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No categories selectedKeywords
gene expressionannotationclustering validationgene ontologyhypergeometric distributionScience & TechnologyTechnologyLife Sciences & BiomedicineAcousticsComputer Science, Artificial IntelligenceComputer Science, CyberneticsEngineering, BiomedicalEngineering, Electrical & ElectronicMathematical & Computational BiologyImaging Science & Photographic TechnologyRadiology, Nuclear Medicine & Medical ImagingTelecommunicationsComputer ScienceEngineeringSEMANTIC SIMILARITY
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