NeMoFinder: dissecting genome-wide protein-protein interactions with meso-scale network motifs

Authors:
Jin Chen;Wynne Hsu;Mong Li Lee;See-Kiong Ng
Affiliations:
National University of Singapore, Singapore;National University of Singapore, Singapore;National University of Singapore, Singapore;Institute for Inforcomm Research, Singapore
Venue:
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2006

Citing 6
Cited 11

gSpan: Graph-Based Substructure Pattern Mining

ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
SPIN: mining maximal frequent subgraphs from graph databases

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs

Bioinformatics
Conserved network motifs allow protein--protein interaction prediction

Bioinformatics
Frequency concepts and pattern detection for the analysis of motifs in networks

Transactions on Computational Systems Biology III

MapReduce-Based Pattern Finding Algorithm Applied in Motif Detection for Prescription Compatibility Network

APPT '09 Proceedings of the 8th International Symposium on Advanced Parallel Processing Technologies
Mining graph patterns efficiently via randomized summaries

Proceedings of the VLDB Endowment
Towards proximity pattern mining in large graphs

Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
A log-linear approach to mining significant graph-relational patterns

Data & Knowledge Engineering
CP-index: on the efficient indexing of large graphs

Proceedings of the 20th ACM international conference on Information and knowledge management
NOVA: a novel and efficient framework for finding subgraph isomorphism mappings in large graphs

DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
An algorithm for network motif discovery in biological networks

International Journal of Data Mining and Bioinformatics
Survey: Computational challenges in systems biology

Computer Science Review
Mining from protein–protein interactions

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Symmetry Compression Method for Discovering Network Motifs

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
G-Tries: a data structure for storing and finding subgraphs

Data Mining and Knowledge Discovery

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recent works in network analysis have revealed the existence of network motifs in biological networks such as the protein-protein interaction (PPI) networks. However, existing motif mining algorithms are not sufficiently scalable to find meso-scale network motifs. Also, there has been little or no work to systematically exploit the extracted network motifs for dissecting the vast interactomes.We describe an efficient network motif discovery algorithm, NeMoFinder, that can mine meso-scale network motifs that are repeated and unique in large PPI networks. Using NeMoFinder, we successfully discovered, for the first time, up to size-12 network motifs in a large whole-genome S. cerevisiae (Yeast) PPI network. We also show that such network motifs can be systematically exploited for indexing the reliability of PPI data that were generated via highly erroneous high-throughput experimental methods.