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ECML
2006
Springer

Unsupervised Multiple-Instance Learning for Functional Profiling of Genomic Data

9 years 2 months ago
Unsupervised Multiple-Instance Learning for Functional Profiling of Genomic Data
Multiple-instance learning (MIL) is a popular concept among the AI community to support supervised learning applications in situations where only incomplete knowledge is available. We propose an original reformulation of the MIL concept for the unsupervised context (UMIL), which can serve as a broader framework for clustering data objects adequately described by the multiple-instance representation. Three algorithmic solutions are suggested by derivation from available conventional methods: agglomerative or partition clustering and MIL's citationkNN approach. Based on standard clustering quality measures, we evaluated these algorithms within a bioinformatic framework to perform a functional profiling of two genomic data sets, after relating expression data to biological annotations into an UMIL representation. Our analysis spotlighted meaningful interaction patterns relating biological processes and regulatory pathways into coherent functional modules, uncovering profound features...
Corneliu Henegar, Karine Clément, Jean-Dani
Added 22 Aug 2010
Updated 22 Aug 2010
Type Conference
Year 2006
Where ECML
Authors Corneliu Henegar, Karine Clément, Jean-Daniel Zucker
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