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  1. Proceedings of the 5th international workshop on Bioinformatics (BIOKDD '05)
  2. Analysis of protein-protein interaction networks using random walks
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Motif discovery for proteins using subsequence clustering
Predicting cancer susceptibility from single-nucleotide polymorphism data: a case study in multiple myeloma
Accelerating DNA sequencing-by-hybridization with noise
Analysis of protein-protein interaction networks using random walks
Graphical models of residue coupling in protein families
On discovery of maximal confident rules without support pruning in microarray data
Finding cliques in protein interaction networks via transitive closure of a weighted graph
A datamining approach to cell population deconvolution from gene expressions using particle filters
Boosting performance of bio-entity recognition by combining results from multiple systems
siRNA off-target search: a hybrid q-gram based filtering approach

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Analysis of protein-protein interaction networks using random walks

Content Provider ACM Digital Library
Author Can, Tolga Singh, Ambuj K. Çamoǧlu, Orhan
Abstract 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.
Starting Page 61
Ending Page 68
Page Count 8
File Format PDF
ISBN 1595932135
DOI 10.1145/1134030.1134042
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2005-08-21
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Pathway membership Random walks on graphs Complex membership Protein networks
Content Type Text
Resource Type Article
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