TY - JOUR
T1 - In silico prediction of human protein interactions using fuzzy-SVM mixture models and its application to cancer research
AU - Chiang, Jung Hsien
AU - Lee, Tsung Lu Michael
N1 - Funding Information:
Manuscript received December 26, 2006; revised May 24, 2007 and September 6, 2007; accepted October 4, 2007. This work was supported in part by the National Science Council, Taiwan, under Grant NSC 91-2321-B-006-003.
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
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U2 - 10.1109/TFUZZ.2007.914041
DO - 10.1109/TFUZZ.2007.914041
M3 - Article
AN - SCOPUS:50549093667
VL - 16
SP - 1087
EP - 1095
JO - IEEE Transactions on Fuzzy Systems
JF - IEEE Transactions on Fuzzy Systems
SN - 1063-6706
IS - 4
ER -