title: Bi-k-bi clustering: mining large scale gene expression data using two-level biclustering.
abstract: Due to the increase in gene expression data sets in recent years, various data mining techniques have been proposed for mining gene expression profiles. However, most of these methods target single gene expression data sets and cannot handle all the available gene expression data in public databases in reasonable amount of time and space. In this paper, we propose a novel framework, bi-k-bi clustering, for finding association rules of gene pairs that can easily operate on large scale and multiple heterogeneous data sets. We applied our proposed framework on the available NCBI GEO Homo sapiens data sets. Our results show consistency and relatedness with the available literature and also provides novel associations.
authors.affiliation: Department of Computer Engineering, Middle East Technical University, Ankara, Turkey. leventc@ceng.metu.edu.tr
authors.forename: Levent
authors.initials: L
authors.lastname: Carkacioğlu
authors.forename: Rengül Cetin
authors.initials: RC
authors.lastname: Atalay
authors.forename: Ozlen
authors.initials: O
authors.lastname: Konu
authors.forename: Volkan
authors.initials: V
authors.lastname: Atalay
authors.forename: Tolga
authors.initials: T
authors.lastname: Can
mesh_terms: D016000:Cluster Analysis; D057225:Data Mining / Q000379:methods*; D030541:Databases, Genetic; D015870:Gene Expression; D020869:Gene Expression Profiling; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis