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.
mixture models support vector machines (svms) fuzzy-svm mixture models (fsmms) fuzzy modeling