title: Querying Co-regulated Genes on Diverse Gene Expression Datasets Via Biclustering.
abstract: Rapid development and increasing popularity of gene expression microarrays have resulted in a number of studies on the discovery of co-regulated genes. One important way of discovering such co-regulations is the query-based search since gene co-expressions may indicate a shared role in a biological process. Although there exist promising query-driven search methods adapting clustering, they fail to capture many genes that function in the same biological pathway because microarray datasets are fraught with spurious samples or samples of diverse origin, or the pathways might be regulated under only a subset of samples. On the other hand, a class of clustering algorithms known as biclustering algorithms which simultaneously cluster both the items and their features are useful while analyzing gene expression data, or any data in which items are related in only a subset of their samples. This means that genes need not be related in all samples to be clustered together. Because many genes only interact under specific circumstances, biclustering may recover the relationships that traditional clustering algorithms can easily miss. In this chapter, we briefly summarize the literature using biclustering for querying co-regulated genes. Then we present a novel biclustering approach and evaluate its performance by a thorough experimental analysis.
authors.affiliation: Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.
authors.forename: Mehmet
authors.initials: M
authors.lastname: Deveci
authors.affiliation: Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.
authors.forename: Onur
authors.initials: O
authors.lastname: Küçüktunç
authors.affiliation: Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.
authors.forename: Kemal
authors.initials: K
authors.lastname: Eren
authors.affiliation: Biomedical Informatics, The Ohio State University, Columbus, OH, USA.
authors.forename: Doruk
authors.initials: D
authors.lastname: Bozdağ
authors.affiliation: Computer Science and Engineering, Sabancı University, Istanbul, Turkey.
authors.forename: Kamer
authors.initials: K
authors.lastname: Kaya
authors.affiliation: Biomedical Informatics, Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA. catalyurek.1@osu.edu.
authors.forename: Ümit V
authors.initials: ÜV
authors.lastname: Çatalyürek
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D015972:Gene Expression Regulation, Neoplastic; D019398:Genes, BRCA1; D024522:Genes, BRCA2; D016158:Genes, p53; D006801:Humans
keywords: Biclustering; Clustering; Gene expression; Microarray