TY - GEN
T1 - A novel evolutionary algorithm for bi-clustering of gene expression data based on the order preserving sub-matrix (OPSM) constraint
AU - Roh, Hongchan
AU - Park, Sanghyun
PY - 2008
Y1 - 2008
N2 - Biclustering is a popular method which can reveal unknown genetic pathways. However, even though many algorithms have been suggested, no overwhelming algorithm has been suggested, due to its significant search space, until now. In this respect, several evolutionary algorithms tried to address this problem utilizing the powerful search capability of Evolutionary Computation (EC). However, most algorithms focused on exploiting the Mean Square Residue (MSR) measure which was proposed by Cheng and Church. The Order Preserving Sub-Matrix (OPSM) constraint was rarely considered even though it promises more biologically relevant biclusters than the MSR measure. The goal of this paper is to design an EC algorithm which ensures biologically significant biclusters by using the OPSM constraint and better biclusters than the original OPSM algorithm. We designed a novel encoding method and evolutionary operators suitable for the OPSM constraint. To efficiently explore the search space, we modulized our evolutionary algorithm and applied the co-evolution concept. Through a set of experiments, it was confirmed that our algorithm outperformed a representative EC biclustering algorithm based on CC and the original OPSM algorithm.
AB - Biclustering is a popular method which can reveal unknown genetic pathways. However, even though many algorithms have been suggested, no overwhelming algorithm has been suggested, due to its significant search space, until now. In this respect, several evolutionary algorithms tried to address this problem utilizing the powerful search capability of Evolutionary Computation (EC). However, most algorithms focused on exploiting the Mean Square Residue (MSR) measure which was proposed by Cheng and Church. The Order Preserving Sub-Matrix (OPSM) constraint was rarely considered even though it promises more biologically relevant biclusters than the MSR measure. The goal of this paper is to design an EC algorithm which ensures biologically significant biclusters by using the OPSM constraint and better biclusters than the original OPSM algorithm. We designed a novel encoding method and evolutionary operators suitable for the OPSM constraint. To efficiently explore the search space, we modulized our evolutionary algorithm and applied the co-evolution concept. Through a set of experiments, it was confirmed that our algorithm outperformed a representative EC biclustering algorithm based on CC and the original OPSM algorithm.
UR - http://www.scopus.com/inward/record.url?scp=67549090422&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=67549090422&partnerID=8YFLogxK
U2 - 10.1109/BIBE.2008.4696685
DO - 10.1109/BIBE.2008.4696685
M3 - Conference contribution
AN - SCOPUS:67549090422
SN - 9781424428458
T3 - 8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008
BT - 8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008
T2 - 8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008
Y2 - 8 October 2008 through 10 October 2008
ER -