Enhanced biclustering on expression data

By Jiong Yang, Haixun Wang, Wei Wang, Philip S. Yu. Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings., 2003

341 citations recorded by the source corpus.

Microarrays are one of the latest breakthroughs in experimental molecular biology, which provide a powerful tool by which the expression patterns of thousands of genes can be monitored simultaneously and are already producing huge amount of valuable data. The concept of bicluster was introduced by Cheng and Church (2000) to capture the coherence of a subset of genes and a subset of conditions. A set of heuristic algorithms were also designed to either find one bicluster or a set of biclusters, which consist of iterations of masking null values and discovered biclusters, coarse and fine node deletion, node addition, and the inclusion of inverted data. These heuristics inevitably suffer from some serious drawback. The masking of null values and discovered biclusters with random numbers may result in the phenomenon of random interference which in turn impacts the discovery of high quality biclusters. To address this issue and to further accelerate the biclustering process, we generalize the model of bicluster to incorporate null values and propose a probabilistic algorithm (FLOC) that can discover a set of k possibly overlapping biclusters simultaneously. Furthermore, this algorithm can easily be extended to support additional features that suit different requirements at virtually little cost. Experimental study on the yeast gene expression data shows that the FLOC algorithm can offer substantial improvements over the previously proposed algorithm.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4fa3ceb0cd2e8d9b3ffbbf26aaa0ea3e0d20e301

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In Molecular Biology

Citations in the corpus

Minimum Sum-Squared Residue Co-Clustering of Gene Expression Data (2004, background): “[18] point out that random numbers used as replacements in [3] can interfere with the future discovery of co-clusters, especially ones that have overlap with the”
The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo (2006, neutral): “Biclustering was developed to address better the full complexity of finding co-regulated genes under multifactor con-trol by grouping genes on the basis of coherence under subsets of observed conditions [10,16-22].”
Biclustering in data mining (2008, methodology): “Yang et al. generalized the deﬁnition of residue used in the node-deletion algorithm to allow missing data entries (i.e., some a ij may be unknown) [22,23].”
Biclustering algorithms for biological data analysis: a survey (2004, neutral): “Yang et al. [29], [30] also used these two datasets.”