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)
Finding molecular complexes through multiple layer clustering of protein interaction networks
International Journal of Bioinformatics Research and Applications
Bridging centrality: graph mining from element level to group level
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
IMPRECO: A Tool for Improving the Prediction of Protein Complexes
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part III
Scalable biomedical and bioinformatics applications
Proceedings of the 3rd international conference on Scalable information systems
International Journal of Data Mining and Bioinformatics
RECOMB 2'09 Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology
IMPRECO: Distributed prediction of protein complexes
Future Generation Computer Systems
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
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
Detection of protein complexes in protein interaction networks using n-clubs
EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Protein function prediction based on patterns in biological networks
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
Protein-to-protein interactions: Technologies, databases, and algorithms
ACM Computing Surveys (CSUR)
A hybrid clustering algorithm for identifying modules in Protein Protein Interaction networks
International Journal of Data Mining and Bioinformatics
Density based merging search of functional modules in protein-protein interaction (PPI) networks
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Clustering with overlap for genetic interaction networks via local search optimization
WABI'11 Proceedings of the 11th international conference on Algorithms in bioinformatics
Computational Biology and Chemistry
Collaboration-based function prediction in protein-protein interaction networks
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Clustering protein interaction data through chaotic genetic algorithm
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Information Sciences: an International Journal
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Identification of conserved protein complexes by module alignment
International Journal of Data Mining and Bioinformatics
An ACO based functional module detection algorithm for protein interaction networks
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Heterodimeric protein complex identification
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Dense subgraphs with restrictions and applications to gene annotation graphs
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
Clustering PPI data based on Improved functional-flow model through Quantum-behaved PSO
International Journal of Data Mining and Bioinformatics
ISBRA'10 Proceedings of the 6th international conference on Bioinformatics Research and Applications
Complex detection based on integrated properties
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
A Coclustering Approach for Mining Large Protein-Protein Interaction Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
EvoBIO'12 Proceedings of the 10th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Computer Science Review
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
International Journal of Bioinformatics Research and Applications
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
Improving protein complex classification accuracy using amino acid composition profile
Computers in Biology and Medicine
Restricted neighborhood search clustering revisited: an evolutionary computation perspective
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
| Hi-index | 3.84 |
Motivation: Understanding principles of cellular organization and function can be enhanced if we detect known and predict still undiscovered protein complexes within the cell's protein--protein interaction (PPI) network. Such predictions may be used as an inexpensive tool to direct biological experiments. The increasing amount of available PPI data necessitates an accurate and scalable approach to protein complex identification. Results: We have developed the Restricted Neighborhood Search Clustering Algorithm (RNSC) to efficiently partition networks into clusters using a cost function. We applied this cost-based clustering algorithm to PPI networks of Saccharomyces cerevisiae, Drosophila melanogaster and Caenorhabditis elegans to identify and predict protein complexes. We have determined functional and graph-theoretic properties of true protein complexes from the MIPS database. Based on these properties, we defined filters to distinguish between identified network clusters and true protein complexes. Conclusions: Our application of the cost-based clustering algorithm provides an accurate and scalable method of detecting and predicting protein complexes within a PPI network. Availability: The RNSC algorithm and data processing code are available upon request from the authors. Supplementary Information: Supplementary data are available at http://www.cs.utoronto.ca/~juris/data/ppi04/