A repeated local search algorithm for biclustering of gene expression data

Authors:
Duy Tin Truong;Roberto Battiti;Mauro Brunato
Affiliations:
University of Trento, Italy;University of Trento, Italy;University of Trento, Italy
Venue:
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
Year:
2013

Citing 4
Cited 0

Biclustering of Expression Data

Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Enhanced Biclustering on Expression Data

BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
Biclustering Algorithms for Biological Data Analysis: A Survey

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Multi-objective evolutionary biclustering of gene expression data

Pattern Recognition

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Abstract

Given a gene expression data matrix where each cell is the expression level of a gene under a certain condition, biclustering is the problem of searching for a subset of genes that coregulate and coexpress only under a subset of conditions. The traditional clustering algorithms cannot be applied for biclustering as one cannot measure the similarity between genes (or rows) and conditions (or columns) by normal geometric similarities. Identifying a network of collaborating genes and a subset of experimental conditions which activate the specific network is a crucial part of the problem. In this paper, the BIClustering problem is solved through a REpeated Local Search algorithm, called BICRELS. The experiments on real datasets show that our algorithm is not only fast but it also significantly outperforms other state-of-the-art algorithms.