Random walk biclustering for microarray data

Authors:
Fabrizio Angiulli;Eugenio Cesario;Clara Pizzuti
Affiliations:
ICAR-CNR, Via P. Bucci 41C, 87036 Rende, CS, Italy;ICAR-CNR, Via P. Bucci 41C, 87036 Rende, CS, Italy;ICAR-CNR, Via P. Bucci 41C, 87036 Rende, CS, Italy
Venue:
Information Sciences: an International Journal
Year:
2008

Citing 14
Cited 5

Noise strategies for improving local search

AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Automatic subspace clustering of high dimensional data for data mining applications

SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Fast algorithms for projected clustering

SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Finding generalized projected clusters in high dimensional spaces

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Co-clustering documents and words using bipartite spectral graph partitioning

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Cluster validity methods: part I

ACM SIGMOD Record
Computers and Intractability: A Guide to the Theory of NP-Completeness

Computers and Intractability: A Guide to the Theory of NP-Completeness
Clustering validity checking methods: part II

ACM SIGMOD Record
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
d-Clusters: Capturing Subspace Correlation in a Large Data Set

ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Information-theoretic co-clustering

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Biclustering Algorithms for Biological Data Analysis: A Survey

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Biclustering in data mining

Computers and Operations Research

Incorporating multiple genomic features with the utilization of interacting domain patterns to improve the prediction of protein-protein interactions

Information Sciences: an International Journal
Noise-robust algorithm for identifying functionally associated biclusters from gene expression data

Information Sciences: an International Journal
Fast algorithm for computing fixpoints of Galois connections induced by object-attribute relational data

Information Sciences: an International Journal
BiMine+: An efficient algorithm for discovering relevant biclusters of DNA microarray data

Knowledge-Based Systems
A new measure for gene expression biclustering based on non-parametric correlation

Computer Methods and Programs in Biomedicine

Quantified Score

Hi-index	0.07

Visualization

Abstract

A biclustering algorithm, based on a greedy technique and enriched with a local search strategy to escape poor local minima, is proposed. The algorithm starts with an initial random solution and searches for a locally optimal solution by successive transformations that improve a gain function. The gain function combines the mean squared residue, the row variance, and the size of the bicluster. Different strategies to escape local minima are introduced and compared. Experimental results on several microarray data sets show that the method is able to find significant biclusters, also from a biological point of view.