Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/42006
Title: Mining deterministic biclusters in gene expression data
Authors: Zhang, Z.
Teo, A.
Ooi, B.C. 
Tan, K.-L. 
Issue Date: 2004
Source: Zhang, Z.,Teo, A.,Ooi, B.C.,Tan, K.-L. (2004). Mining deterministic biclusters in gene expression data. Proceedings - Fourth IEEE Symposium on Bioinformatics and Bioengineering, BIBE 2004 : 283-290. ScholarBank@NUS Repository.
Abstract: A bicluster of a gene expression dataset captures the coherence of a subset of genes and a subset of conditions. Biclustering algorithms are used to discover biclusters whose subset of genes are co-regulated under subset of conditions. In this paper, we present a novel approach, called DBF (Deterministic Biclustering with Frequent pattern mining) to finding biclusters. Our scheme comprises two phases. In the first phase, we generate a set of good quality biclusters based on frequent pattern mining. In the second phase, the biclusters are further iteratively refined (enlarged) by adding more genes and/or conditions. We evaluated our scheme against FLOC and our results show that DBF can generate larger and better biclusters.
Source Title: Proceedings - Fourth IEEE Symposium on Bioinformatics and Bioengineering, BIBE 2004
URI: http://scholarbank.nus.edu.sg/handle/10635/42006
ISBN: 0769521738
Appears in Collections:Staff Publications

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