Computational Biology and Chemistry
Dynamical Systems for Discovering Protein Complexes and Functional Modules from Biological Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Computational Biology and Chemistry
Dividing Protein Interaction Networks by Growing Orthologous Articulations
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Drug targets for tumorigenesis: Insights from structural analysis of EGFR signaling network
Journal of Biomedical Informatics
Fast algorithms for detecting overlapping functional modules in protein-protein interaction networks
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Identifying the overlapping complexes in protein interaction networks
International Journal of Data Mining and Bioinformatics
Weighted cohesiveness for identification of functional modules and their interconnectivity
BIRD'07 Proceedings of the 1st international conference on Bioinformatics research and development
PINCoC: a co-clustering based approach to analyze protein-protein interaction networks
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
A graph-theoretic method for mining overlapping functional modules in protein interaction networks
ISBRA'08 Proceedings of the 4th international conference on Bioinformatics research and applications
Divide, align and full-search for discovering conserved protein complexes
EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Protein-to-protein interactions: Technologies, databases, and algorithms
ACM Computing Surveys (CSUR)
Dividing protein interaction networks for modular network comparative analysis
Pattern Recognition Letters
A hybrid clustering algorithm for identifying modules in Protein Protein Interaction networks
International Journal of Data Mining and Bioinformatics
Computational Biology and Chemistry
Computational Biology and Chemistry
SimBioNeT: A Simulator of Biological Network Topology
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
ISBRA'10 Proceedings of the 6th international conference on Bioinformatics Research and Applications
Assessing significance of connectivity and conservation in protein interaction networks
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
ProRank: a method for detecting protein complexes
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Detecting protein complexes from noisy protein interaction data
Proceedings of the 11th International Workshop on Data Mining in Bioinformatics
Mining from protein–protein interactions
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
A supervised approach to detect protein complex by combining biological and topological properties
International Journal of Data Mining and Bioinformatics
| Hi-index | 3.84 |
Motivation: The building blocks of biological networks are individual protein--protein interactions (PPIs). The cumulative PPI data set in Saccharomyces cerevisiae now exceeds 78 000. Studying the network of these interactions will provide valuable insight into the inner workings of cells. Results: We performed a systematic graph theory-based analysis of this PPI network to construct computational models for describing and predicting the properties of lethal mutations and proteins participating in genetic interactions, functional groups, protein complexes and signaling pathways. Our analysis suggests that lethal mutations are not only highly connected within the network, but they also satisfy an additional property: their removal causes a disruption in network structure. We also provide evidence for the existence of alternate paths that bypass viable proteins in PPI networks, while such paths do not exist for lethal mutations. In addition, we show that distinct functional classes of proteins have differing network properties. We also demonstrate a way to extract and iteratively predict protein complexes and signaling pathways. We evaluate the power of predictions by comparing them with a random model, and assess accuracy of predictions by analyzing their overlap with MIPS database. Conclusions: Our models provide a means for understanding the complex wiring underlying cellular function, and enable us to predict essentiality, genetic interaction, function, protein complexes and cellular pathways. This analysis uncovers structure--function relationships observable in a large PPI network. Supplementary information: We are placing the full predicted tables on the web page: http://www.cs.utoronto.ca/~juris/data/b03/SuppDataTables.zip