Please use this identifier to cite or link to this item: https://doi.org/10.1109/TCBB.2012.58
Title: Detection of outlier residues for improving interface prediction in protein heterocomplexes
Authors: Chen, P.
Wong, L. 
Li, J. 
Keywords: Outlier detection
protein-protein interaction
SVM ensemble
Issue Date: 2012
Source: Chen, P., Wong, L., Li, J. (2012). Detection of outlier residues for improving interface prediction in protein heterocomplexes. IEEE/ACM Transactions on Computational Biology and Bioinformatics 9 (4) : 1155-1165. ScholarBank@NUS Repository. https://doi.org/10.1109/TCBB.2012.58
Abstract: 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. © 2012 IEEE.
Source Title: IEEE/ACM Transactions on Computational Biology and Bioinformatics
URI: http://scholarbank.nus.edu.sg/handle/10635/39483
ISSN: 15455963
DOI: 10.1109/TCBB.2012.58
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

14
checked on Dec 11, 2017

WEB OF SCIENCETM
Citations

11
checked on Dec 11, 2017

Page view(s)

55
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.