Site Search
Computer Science

Photos

Christie Ezeife, Ph.D.Dr. Christie Ezeife
Dr. Christie Ezeife
Arunita Jaekel, Ph.D.Dr. Arunita Jaekel
Dr. Arunita Jaekel
Dr. Luis RuedaDr. Luis Rueda
Dr. Luis Rueda
Robin Gras, Ph.D.Dr. Robin Gras
Dr. Robin Gras
Windsor WaterfrontWindsor Waterfront Park
Windsor Waterfront Park
Imran Ahmad, Ph.D.Dr. Imran Ahmad
Dr. Imran Ahmad
Dr. Scott GoodwinDr. Scott Goodwin
Dr. Scott Goodwin
Alioune Ngom, Ph.D.Dr. Alioune Ngom
Dr. Alioune Ngom
Xiaobu Yuan, Ph.D.Dr. Xiaobu Yuan
Dr. Xiaobu Yuan
Dr. Ziad Kobti lecturingDr. Ziad Kobti
Dr. Ziad Kobti
Dr. Robert KentDr. Robert Kent
Dr. Robert Kent
Lambton TowerLambton Tower
Lambton Tower
Jessica Chen, Ph.D.Dr. Jessica Chen
Dr. Jessica Chen

The Relative Vertex Clustering Value - A New Criterion for the Fast Discovery of Functional Modules in Protein Interaciton Networks

Add this event into your calendar using the iCAL format
  • Fri, 03/17/2017 - 11:00am - 12:00pm




Computer Science Colloquium Series
Dr. Alioune Ngom

Date:  Friday, March 17th, 2017
Time: 11:00 am
Location: Chrysler Hall North, G100

 Abstract: Cellular processes are known to be modular and are realized by groups of proteins implicated in common biological functions. Such groups of proteins are called functional modules, and many community detection methods have been devised for their discovery from protein interaction networks (PINs) data. In current agglomerative clustering approaches, vertices with just a very few neighbors are often classified as separate clusters, which does not make sense biologically. Also, a major limitation of agglomerative techniques is that their computational efficiency do not scale well to large PINs. Finally, PIN data obtained from large scale experiments generally contain many false positives, and this makes it hard for agglomerative clustering methods to find the correct clusters, since they are known to be sensitive to noisy data. We propose a local similarity premetric, the relative vertex clustering value, as a new criterion allowing to decide when a node can be added to a given node’s cluster and which addresses the above three issues. Based on this criterion, we introduce a novel and very fast agglomerative clustering technique, FAC-PIN, for discovering functional modules and protein complexes from a PIN data. Our proposed FAC-PIN algorithm is applied to nine PIN data from eight different species including the yeast PIN, and the identified functional modules are validated using Gene Ontology (GO) annotations from DAVID Bioinformatics Resources. Identified protein complexes are also validated using experimentally verified complexes. Computational results show that FAC-PIN can discover functional modules or protein complexes from PINs more accurately and more efficiently than HC-PIN and CNM, the current state-of-the-art approaches for clustering PINs in an agglomerative manner.

Bio: Alioune Ngom received his BSc degree in Computer Science from the Universite du Quebec a Trois-Rivieres in 1992, and his MSc and PhD degrees in Computer Science from the University of Ottawa in 1995 and 1998, respectively. He has held an Assistant Professor position in the Department of Computer Science at Lakehead University, from 1998 to 2000. He is currently a Full Professor within the School of Computer Science at the University of Windsor. His main research interest focus on the study, development, and application of machine learning approaches in Bioinformatics. His current research includes bio-molecular network reconstruction, network-based machine learning, sparse representation learning, and cancer bioinformatics. He holds one patent and has published more than 100 publications in journals and conference proceedings, in the areas of machine learning and bioinformatics. He is a member of the International Society for Computational Biology.



csgradinfo@uwindsor.ca
(519)253-3000


See More: