In silico prediction of human protein interactions using fuzzy-SVM mixture models and its application to cancer research

Proteomics technologies and bioinformatics tools have been widely used to analyze protein-protein interactions of complex biological systems, which are essential for understanding the mechanisms of human and cancer biology. Although many studies have tackled the problem of high-throughput protein-protein interaction identifications in Saccharomyces cerevisiae, Caenorhabditis elegans, and Drosophila melanogaster, the effort to predict human and cancer-related protein-protein interaction is still limited. Moreover, low consistency and high false positive rates are major drawbacks of these high-throughput methods. In this research, the focus is on predicting human cancer-related protein-protein interaction and reducing false positive rates with integrated classifiers. We propose a hybrid machine learning system by merging fuzzy multiset-based classifiers and support vector machines (SVMs) into fuzzy-SVM mixture models (FSMMs). Our experimental result of the FSMMs approach achieves consistent prediction accuracy on human protein-protein interactions (PPIs) with an receiver operating curve score of 0.965 that outperforms other models. Overall, prediction results on cancer-related protein pairs indicate that our proposed system is effective for identifying both known and novel PPIs to assist cancer research in discovering novel interactions.