title: Bi-level clustering of mixed categorical and numerical biomedical data.
abstract: Biomedical data sets often have mixed categorical and numerical types, where the former represent semantic information on the objects and the latter represent experimental results. We present the BILCOM algorithm for 'Bi-Level Clustering of Mixed categorical and numerical data types'. BILCOM performs a pseudo-Bayesian process, where the prior is categorical clustering. BILCOM partitions biomedical data sets of mixed types, such as hepatitis, thyroid disease and yeast gene expression data with Gene Ontology annotations, more accurately than if using one type alone.
authors.affiliation: Department of Computer Science and Engineering, York University, M3J1P3, Toronto, Ontario, Canada. billa@cs.yorku.ca
authors.forename: Bill
authors.initials: B
authors.lastname: Andreopoulos
authors.forename: Aijun
authors.initials: A
authors.lastname: An
authors.forename: Xiaogang
authors.initials: X
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D035843:Biomedical Research; D001330:Electronic Data Processing / Q000379:methods*; D015966:Gene Expression Regulation, Fungal / Q000235:genetics; D016681:Genome, Fungal / Q000235:genetics; D006505:Hepatitis / Q000235:genetics; D006801:Humans; D012441:Saccharomyces cerevisiae / Q000235:genetics; D013959:Thyroid Diseases / Q000235:genetics