Issue No. 04 - July-Aug. (2012 vol. 9)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2012.58
Limsoon Wong , Sch. of Comput., Nat. Univ. of Singapore, Singapore, Singapore
Peng Chen , Inst. of Intell. Machines, Hefei, China
Jinyan Li , Sch. of Comput., Nat. Univ. of Singapore, Singapore, Singapore
Sequence-based understanding and identification of protein binding interfaces is a challenging research topic due to the complexity in protein systems and the imbalanced distribution between interface and noninterface residues. This paper presents an outlier detection idea to address the redundancy problem in protein interaction data. The cleaned training data are then used for improving the prediction performance. We use three novel measures to describe the extent a residue is considered as an outlier in comparison to the other residues: the distance of a residue instance from the center instance of all residue instances of the same class label (Dist), the probability of the class label of the residue instance (PCL), and the importance of within-class and between-class (IWB) residue instances. Outlier scores are computed by integrating the three factors; instances with a sufficiently large score are treated as outliers and removed. The data sets without outliers are taken as input for a support vector machine (SVM) ensemble. The proposed SVM ensemble trained on input data without outliers performs better than that with outliers. Our method is also more accurate than many literature methods on benchmark data sets. From our empirical studies, we found that some outlier interface residues are truly near to noninterface regions, and some outlier noninterface residues are close to interface regions.
support vector machines, benchmark testing, bioinformatics, molecular biophysics, proteins, noninterface region, interface prediction, protein heterocomplex, sequence-based understanding, protein binding interface, protein systems, redundancy problem, protein interaction data, residue distance, residue instance probability, support vector machine ensemble, SVM ensemble, benchmark data sets, outlier interface residues, Training, Proteins, Support vector machines, Training data, Educational institutions, Vectors, Bioinformatics, SVM ensemble., Outlier detection, protein-protein interaction
Limsoon Wong, Peng Chen and Jinyan Li, "Detection of Outlier Residues for Improving Interface Prediction in Protein Heterocomplexes," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. , pp. 1155-1165, 2012.