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|Title:||Mining deterministic biclusters in gene expression data|
|Citation:||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|
|Appears in Collections:||Staff Publications|
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