A Latent Feature Model Approach to Biclustering

José Caldas, Samuel Kaski

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Biclustering is the unsupervised learning task of mining a data matrix for useful submatrices, for instance groups of genes that are co-expressed under particular biological conditions. As these submatrices are expected to partly overlap, a significant challenge in biclustering is to develop methods that are able to detect overlapping biclusters. The authors propose a probabilistic mixture modelling framework for biclustering biological data that lends itself to various data types and allows biclusters to overlap. Their framework is akin to the latent feature and mixture-of-experts model families, with inference and parameter estimation being performed via a variational expectation-maximization algorithm. The model compares favorably with competing approaches, both in a binary DNA copy number variation data set and in a miRNA expression data set, indicating that it may potentially be used as a general-problem solving tool in biclustering.
Original languageEnglish
JournalInternational Journal of Knowledge Discovery in Bioinformatics
Volume6
Issue number2
DOIs
Publication statusPublished - 2016
MoE publication typeA1 Journal article-refereed

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