Predicting protein-protein interactions using numerical associational features

Authors:
Waleed Aljandal;William H. Hsu;Jing Xia
Affiliations:
Department of Computing and Information Sciences, Kansas State University, Manhattan, KS;Department of Computing and Information Sciences, Kansas State University, Manhattan, KS;Department of Computing and Information Sciences, Kansas State University, Manhattan, KS
Venue:
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Year:
2009

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Abstract

We investigate the problem of predicting proteinprotein interaction (PPI) using numerical features constructed from parent-child relation of a partial network constructed from known protein interactions. For each pair of proteins, we use a validationbased approach to normalize these features, which are based on association rule interestingness measures. The primary contribution of this work is the parametric normalization formula we derive and calibrate using data for the PPI task. This formula improves basic interestingness measures through taking sizes of itemset into account. Our derived itemset size-sensitive measures consider those rare but significant relationships among the children and the parents of set of proteins. We evaluate our work using k-nearest neighbor and rule-based classification approach.