NDLI: Detection of Outlier Residues for Improving Interface Prediction in Protein Heterocomplexes

Content Provider	IEEE Xplore Digital Library
Author	Peng Chen Limsoon Wong Jinyan Li
Copyright Year	2004
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.
Sponsorship	IEEE Computer Society
Page Count	11
File Size	1538786
Starting Page	1155
Ending Page	1165
File Format	PDF
ISSN	15455963
Volume Number	9
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Proteins Support vector machines Training data Educational institutions Vectors Bioinformatics SVM ensemble. Outlier detection protein-protein interaction
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Genetics Biotechnology

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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