Efficient Detection of Network Motifs
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A multi-layered approach to protein data integration for diabetes research
Artificial Intelligence in Medicine
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GOSAP: Gene Ontology-Based Semantic Alignment of Biological Pathways
International Journal of Bioinformatics Research and Applications
RAM: Randomized Approximate Graph Mining
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GADDI: distance index based subgraph matching in biological networks
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Frequent subgraph pattern mining on uncertain graph data
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MARGIN: Maximal frequent subgraph mining
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Full duplicate candidate pruning for frequent connected subgraph mining
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DESSIN: mining dense subgraph patterns in a single graph
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Frequent approximate subgraphs as features for graph-based image classification
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SubMAP: aligning metabolic pathways with subnetwork mappings
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RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
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RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
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
Analyzing incomplete biological pathways using network motifs
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
| Hi-index | 3.84 |
Motivation: With rapidly increasing amount of network and interaction data in molecular biology, the problem of effectively analyzing this data is an important one. Graph theoretic formalisms, commonly used for these analysis tasks, often lead to computationally hard problems due to their relation with subgraph isomorphism. Results: This paper presents an innovative new algorithm for detecting frequently occurring patterns and modules in biological networks. Using an innovative graph simplification technique, which is ideally suited to biological networks, our algorithm renders these problems computationally tractable. Indeed, we show experimentally that our algorithm can extract frequently occurring patterns in metabolic pathways extracted from the KEGG database within seconds. The proposed model and algorithm are applicable to a variety of biological networks either directly or with minor modifications. Availability: Implementation of the proposed algorithms in the C programming language is available as open source at http://www.cs.purdue.edu/homes/koyuturk/pathway/