The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Efficient algorithms for detecting signaling pathways in protein interaction networks
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
Molecular Function Prediction Using Neighborhood Features
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Learning protein functions from bi-relational graph of proteins and function annotations
WABI'11 Proceedings of the 11th international conference on Algorithms in bioinformatics
Analyzing incomplete biological pathways using network motifs
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Top-k Similar Graph Matching Using TraM in Biological Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Seed-weighted random walk ranking for cancer biomarker prioritisation: a case study in leukaemia
International Journal of Data Mining and Bioinformatics
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Genome wide protein networks have become reality in recent years due to high throughput methods for detecting protein interactions. Recent studies show that a networked representation of proteins provides a more accurate model of biological systems and processes compared to conventional pair-wise analyses. Complementary to the availability of protein networks, various graph analysis techniques have been proposed to mine these networks for pathway discovery, function assignment, and prediction of complex membership. In this paper, we propose using random walks on graphs for the complex/pathway membership problem. We evaluate the proposed technique on three different probabilistic yeast networks using a benchmark dataset of 27 complexes from the MIPS complex catalog database and 10 pathways from the KEGG pathway database. Furthermore, we compare the proposed technique to two other existing techniques both in terms of accuracy and running time performance, thus addressing the scalability issue of such analysis techniques for the first time. Our experiments show that the random walk technique achieves similar or better accuracy with more than 1,000 times speed-up compared to the best competing technique.