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 outcomes as predictive feedback for rational model selection. Computational results suggest that one-class SVM is more suited to be used as negative data sampling method than two-class PPI predictor, and the predictive feedback constrained model selection helps to yield a rational predictive model that reduces the risk of false positive predictions. Some predictions have been validated by the recent literature. Lastly, gene ontology based clustering of the predicted PPI networks is conducted to provide valuable cues for the pathogenesis of HTLV retrovirus. P rotein-protein interaction (PPI) plays an important role in mediating biological processes, cellular signaling pathways and development of organismal systems. Accurate mapping of the proteome-wide interactome is a central problem of proteomics and system biology. Although recent years have witnessed much progress in experimental identification and computational prediction of PPIs 1 , high risk of false discovery rate is still a problem to be effectively addressed 1,2 . For instances, in vitro detection methods such as affinity purification are prone to capture false interactions, in vivo yeast two-hybrid (Y2H) is likely biased towards non-specific interactions 3 and gene co-expression that could induce synthetic lethality is not efficient to detect pathogen-host protein interactions 4, 5 . Recent critical assessments of experimentally obtained PPI data suggest that these data exhibit an unacceptably high fraction of false positives and low agreement between each other [6] [7] [8] . Meanwhile, computational methods also takes the risk of high false discovery rate for the following reasons. Firstly, the experimentally identified PPI data are likely to contain a certain level of noise (false interactions). Secondly, the negative data needed for two-class PPI prediction are usually obtained by random sampling [9] [10] [11] [12] [13] [14] [15] , which may introduce considerable false negative. Thirdly, model selection is generally conducted by cross validation on the training PPI data, and the trained models, if used for proteome-wide predictions, are prone to overpredictions. For pathogen-host PPI prediction, these issues become worse because the training data available are much smaller and less representative. Thus the intra-species models [12] [13] [14] [15] [16] [17] [18] [19] [20] are likely to yield more false positive predictions than the inter-species PPI prediction models [9] [10] [11] . At present the negative data required for computational reconstruction of PPI networks are in general not available. Recently some negative data from biological experiments have been collected into database, e.g. the reference set of negatome 21 , but the negative data are not enough train a two-class classifier. To meet the need of computational modeling, random sampling is often used to generate negative data [9] [10] [11] [12] [13] [14] [15] . The assumption behind random sampling is that the non-interactome space is much larger than the interactome space, so that random sampling could hit the non-interactome space with a large probability to sample true negatives (non-interac-OPEN
Some proposed negative data sampling methods and selection strategies are also conducted REF .
A novel one-class SVM based negative data sampling method for reconstructing proteome-wide HTLV-human protein interaction networks
Scientific Reports
Scientific Reports
Medicine Computer Science