# Stellar-Mass Black Hole Optimization for Biclustering Microarray Gene Expression Data

@article{Rengeswaran2015StellarMassBH,
title={Stellar-Mass Black Hole Optimization for Biclustering Microarray Gene Expression Data},
author={Balamurugan Rengeswaran and A. M. Natarajan and Kandhasamy Premalatha},
journal={Applied Artificial Intelligence},
year={2015},
volume={29},
pages={353 - 381}
}
• Published 1 April 2015
• Computer Science
• Applied Artificial Intelligence
DNA microarray gene expression data analysis has provided new insights into gene function, disease pathophysiology, disease classification, and drug development. Biclustering in gene expression data is a subset of the genes demonstrating consistent patterns over a subset of the conditions. The proposed work finds the significant biclusters in large expression data using a novel optimization technique called stellar-mass black hole optimization (SBO). This optimization algorithm is inspired from…
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## References

SHOWING 1-10 OF 38 REFERENCES
Parallelized Evolutionary Learning for Detection of Biclusters in Gene Expression Data
• Computer Science
IEEE/ACM Transactions on Computational Biology and Bioinformatics
• 2012
This paper proposes a new biclustering algorithm based on evolutionary learning that demonstrates a significant improvement in discovering additive biclusters and is able to discover bicluster seeds within a limited computing time.
Evolutionary Algorithm Based on New Crossover for the Biclustering of Gene Expression Data
• Computer Science
PRIB
• 2014
A new evolutionary algorithm based on a new crossover method, dedicated to the biclustering of gene expression data is proposed, that extracts high quality biclusters with highly correlated genes that are particularly involved in specific ontology structure.
Biclustering of microarray data with MOSPO based on crowding distance
• Computer Science
BMC Bioinformatics
• 2009
This work presents the CMOPSOB (Crowding distance based Multi-objective Particle Swarm Optimization Biclustering), a novel clustering approach for microarray datasets to cluster genes and conditions highly related in sub-portions of the microarray data.
Biclustering of expression data with evolutionary computation
• Computer Science
IEEE Transactions on Knowledge and Data Engineering
• 2006
The approach, named SEBI, is based on evolutionary algorithms, which have been proven to have excellent performance on complex problems, and searches for biclusters following a sequential covering strategy, and shows an excellent performance at finding patterns in gene expression data.
Discovering statistically significant biclusters in gene expression data
• Computer Science
ISMB
• 2002
A new method to detect significant biclusters in large expression datasets is proposed and is able to detect and relate finer tissue types than was previously possible in cancer data and outperforms the biclustering algorithm of Cheng and Church (2000).
An EA framework for biclustering of gene expression data
• Computer Science
Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)
• 2004
This paper proposes a general framework that embed such biclustering methods as local search procedures in an evolutionary algorithm and demonstrates on one prominent example that this approach achieves significant improvements in the quality of the biclusters when compared to the application of the greedy strategy alone.
On biclustering of gene expression data
• Computer Science
2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS)
• 2015
This survey introduced some definitions of the biclustering, a non-supervised technique that outperforms the traditional clustering techniques because it can group both genes and conditions in the same time.