A novel one-class SVM based negative data sampling method for reconstructing proteome-wide HTLV-human protein interaction networks.

ABSTRACT: Protein-protein interaction (PPI) prediction is generally treated as a problem of binary classification wherein negative data sampling is still an open problem to be addressed. The commonly used random sampling is prone to yield less representative negative data with considerable false negatives. Meanwhile rational constraints are seldom exerted on model selection to reduce the risk of false positive predictions for most of the existing computational methods. In this work, we propose a novel negative data sampling method based on one-class SVM (support vector machine, SVM) to predict proteome-wide protein interactions between HTLV retrovirus and Homo sapiens, wherein one-class SVM is used to choose reliable and representative negative data, and two-class SVM is used to yield proteome-wide

SUBMITTER: Mei S 

PROVIDER: S-EPMC5379509 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature