Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Biclustering of Expression Data with Evolutionary Computation
IEEE Transactions on Knowledge and Data Engineering
Multi-objective evolutionary biclustering of gene expression data
Pattern Recognition
PISA: a platform and programming language independent interface for search algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
BiHEA: A Hybrid Evolutionary Approach for Microarray Biclustering
BSB '09 Proceedings of the 4th Brazilian Symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology
Iterated local search for biclustering of microarray data
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
BiMine+: An efficient algorithm for discovering relevant biclusters of DNA microarray data
Knowledge-Based Systems
A Discrete Artificial Bees Colony Inspired Biclustering Algorithm
International Journal of Swarm Intelligence Research
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In this paper, a new memetic approach that integrates a Multi-Objective Evolutionary Algorithm (MOEA) with local search for microarray biclustering is presented. The original features of this proposal are the consideration of opposite regulation and incorporation of a mechanism for tuning the balance between the size and row variance of the biclusters. The approach was developed according to the Platform and Programming Language Independent Interface for Search Algorithms (PISA) framework, thus achieving the possibility of testing and comparing several different memetic MOEAs. The performance of the MOEA strategy based on the SPEA2 performed better, and its resulting biclusters were compared with those obtained by a multi-objective approach recently published. The benchmarks were two datasets corresponding to Saccharomyces cerevisiae and human B-cells Lymphoma . Our proposal achieves a better proportion of coverage of the gene expression data matrix, and it also obtains biclusters with new features that the former existing evolutionary strategies can not detect.