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Discovering Coherent Biclusters from Gene Expression Data Using Zero-Suppressed Binary Decision Diagrams
October-December 2005 (vol. 2 no. 4)
pp. 339-354

Abstract—The biclustering method can be a very useful analysis tool when some genes have multiple functions and experimental conditions are diverse in gene expression measurement. This is because the biclustering approach, in contrast to the conventional clustering techniques, focuses on finding a subset of the genes and a subset of the experimental conditions that together exhibit coherent behavior. However, the biclustering problem is inherently intractable, and it is often computationally costly to find biclusters with high levels of coherence. In this work, we propose a novel biclustering algorithm that exploits the zero-suppressed binary decision diagrams (ZBDDs) data structure to cope with the computational challenges. Our method can find all biclusters that satisfy specific input conditions, and it is scalable to practical gene expression data. We also present experimental results confirming the effectiveness of our approach.

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Index Terms:
Clustering, life and medical sciences, bioinformatics (genome or protein) databases, logic design.
Citation:
Sungroh Yoon, Christine Nardini, Luca Benini, Giovanni De Micheli, "Discovering Coherent Biclusters from Gene Expression Data Using Zero-Suppressed Binary Decision Diagrams," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 2, no. 4, pp. 339-354, Oct.-Dec. 2005, doi:10.1109/TCBB.2005.55
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