ArticleBMC bioinformatics2012
A probabilistic coevolutionary biclustering algorithm for discovering coherent patterns in gene expression dataset.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Network pharmacology strategies toward multi-target anticancer therapies: from computational models to experimental design principles.Current pharmaceutical design · 2014Review
- InCoB2012 Conference: from biological data to knowledge to technological breakthroughs.BMC bioinformatics · 2012Article
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4 authors.
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Abstract
backgroundBiclustering has been utilized to find functionally important patterns in biological problem. Here a bicluster is a submatrix that consists of a subset of rows and a subset of columns in a matrix, and contains homogeneous patterns. The problem of finding biclusters is still challengeable due to computational complex trying to capture patterns from two-dimensional features.
resultsWe propose a Probabilistic COevolutionary Biclustering Algorithm (PCOBA) that can cluster the rows and columns in a matrix simultaneously by utilizing a dynamic adaptation of multiple species and adopting probabilistic learning. In biclustering problems, a coevolutionary search is suitable since it can optimize interdependent subcomponents formed of rows and columns. Furthermore, acquiring statistical information on two populations using probabilistic learning can improve the ability of search towards the optimum value. We evaluated the performance of PCOBA on synthetic dataset and yeast expression profiles. The results demonstrated that PCOBA outperformed previous evolutionary computation methods as well as other biclustering methods.
conclusionsOur approach for searching particular biological patterns could be valuable for systematically understanding functional relationships between genes and other biological components at a genome-wide level.
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