TY - GEN
T1 - Detecting protein complexes using gene expression biclusters
AU - Hanna, Eileen Marie
AU - Zaki, Nazar M.
PY - 2015/10/16
Y1 - 2015/10/16
N2 - The importance of detecting protein complexes in protein interaction networks originates from the fact that they are key players in most cellular processes. The more complexes we identify, the better we can understand normal as well as abnormal molecular events. Despite the notable performance of the current computational methods for detecting protein complexes, questions arise regarding potential ways to improve them, in addition to ameliorative guidelines to introduce novel approaches. A close interpretation leads to the assent that the way in which protein interaction networks are initially viewed should be adjusted. These networks are dynamic in reality and it is necessary to consider this fact to enhance the detection of complexes. In this paper, we present "DyCluster", a framework to model dynamic aspect of protein interaction networks by incorporating gene expression data, through biclustering techniques, prior to applying complex-detection algorithms. The experimental results show that DyCluster leads to higher numbers of correctly-detected complexes with better evaluation scores.
AB - The importance of detecting protein complexes in protein interaction networks originates from the fact that they are key players in most cellular processes. The more complexes we identify, the better we can understand normal as well as abnormal molecular events. Despite the notable performance of the current computational methods for detecting protein complexes, questions arise regarding potential ways to improve them, in addition to ameliorative guidelines to introduce novel approaches. A close interpretation leads to the assent that the way in which protein interaction networks are initially viewed should be adjusted. These networks are dynamic in reality and it is necessary to consider this fact to enhance the detection of complexes. In this paper, we present "DyCluster", a framework to model dynamic aspect of protein interaction networks by incorporating gene expression data, through biclustering techniques, prior to applying complex-detection algorithms. The experimental results show that DyCluster leads to higher numbers of correctly-detected complexes with better evaluation scores.
UR - http://www.scopus.com/inward/record.url?scp=84953410775&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84953410775&partnerID=8YFLogxK
U2 - 10.1109/CIBCB.2015.7300271
DO - 10.1109/CIBCB.2015.7300271
M3 - Conference contribution
AN - SCOPUS:84953410775
T3 - 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015
BT - 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015
Y2 - 12 August 2015 through 15 August 2015
ER -