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Inferring protein interactions from experimental data by association probabilistic method
Author(s) -
Chen Luonan,
Wu LingYun,
Wang Yong,
Zhang XiangSun
Publication year - 2006
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.20783
Subject(s) - probabilistic logic , inference , computer science , association (psychology) , data mining , association rule learning , pearson product moment correlation coefficient , protein–protein interaction , simple (philosophy) , machine learning , artificial intelligence , mathematics , statistics , biology , genetics , philosophy , epistemology
Abstract To elucidate protein interaction networks is one of the major goals of functional genomics for whole organisms. So far, various computational methods have been proposed for inference of protein–protein interactions. Based on the association method by Sprinzak et al., we propose an association probabilistic method in this short communication to infer protein interactions directly from the experimental data, which outperformed other existing methods in terms of both accuracy and efficiency despite its simple form. Specifically, we show that the association probabilistic method achieves the highest accuracy among the existing approaches for the measures of root‐mean‐square error and the Pearson correlation coefficient, and also runs much faster than the LP‐based method, by experimental dataset in Yeast. Software is available from the authors upon request. Proteins 2006. © 2006 Wiley‐Liss, Inc.