Mining deterministic biclusters in gene expression data

被引：0
|
作者：
Zhang, ZH [1 ]
Teo, A [1 ]
Ooi, BC [1 ]
Tan, KL [1 ]
机构：
[1] Natl Univ Singapore, Dept Comp Sci, Singapore, Singapore
来源：
BIBE 2004: FOURTH IEEE SYMPOSIUM ON BIOINFORMATICS AND BIOENGINEERING, PROCEEDINGS | 2004年
关键词：
D O I：
暂无
中图分类号：
Q5 [生物化学]; Q7 [分子生物学];
学科分类号：
071010 ; 081704 ;
摘要：
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.
引用
收藏
页码：283 / 290
页数：8
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