Title: A consensus method for prioritising drug-associated target proteins

Authors: Gang Shu; Xiaodi Huang; Shanfeng Zhu

Addresses: The School of Computer Science and Shanghai Key Lab. of Intelligent Information Processing, Fudan University, Shanghai 200433, China ' School of Computing and Mathematics, Charles Sturt University, Albury, NSW 2640, Australia ' The School of Computer Science and Shanghai Key Lab. of Intelligent Information Processing, Fudan University, Shanghai 200433, China

Abstract: It is generally believed that the degree of a relation between two entities is likely to be stronger if they co-occur more often in the literature. Based on this assumption, several methods are used in biomedical text mining such as support, confidence, chi-square, odds ratio, lift, all-confidence, coherence, and pof. Comparing these eight methods, our work aims to find the best one. Also, we present a consensus approach that can further improve the performance. Experimental results on prioritising drug targets have shown that pof, coherence, and all-confidence in sequence are the top three. By integrating coherence into pof, the consensus method is the best one among all compared methods.

Keywords: biomedical text mining; data mining; drug target prioritisation; consensus method; drug targets.

DOI: 10.1504/IJDMB.2012.048197

International Journal of Data Mining and Bioinformatics, 2012 Vol.6 No.2, pp.178 - 195

Published online: 17 Dec 2014 *

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