Detection of outlier residues for improving interface prediction in protein heterocomplexes
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...
Saved in:
Main Authors: | , , |
---|---|
Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2013
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/103829 http://hdl.handle.net/10220/16551 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-103829 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1038292020-05-28T07:17:30Z Detection of outlier residues for improving interface prediction in protein heterocomplexes Chen, Peng Wong, Limsoon Li, Jinyan School of Computer Engineering Bioinformatics Research Centre DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences 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. 2013-10-17T04:31:08Z 2019-12-06T21:21:13Z 2013-10-17T04:31:08Z 2019-12-06T21:21:13Z 2012 2012 Journal Article Chen, P., Wong, L. S., & Li, J. Y. (2012). Detection of outlier residues for improving interface prediction in protein heterocomplexes. IEEE/ACM transactions on computational biology and bioinformatics, 9(4), 1155-1165. 1545-5963 https://hdl.handle.net/10356/103829 http://hdl.handle.net/10220/16551 10.1109/TCBB.2012.58 en IEEE/ACM transactions on computational biology and bioinformatics © 2012 IEEE |
institution |
Nanyang Technological University |
building |
NTU Library |
country |
Singapore |
collection |
DR-NTU |
language |
English |
topic |
DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences |
spellingShingle |
DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences Chen, Peng Wong, Limsoon Li, Jinyan Detection of outlier residues for improving interface prediction in protein heterocomplexes |
description |
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. |
author2 |
School of Computer Engineering |
author_facet |
School of Computer Engineering Chen, Peng Wong, Limsoon Li, Jinyan |
format |
Article |
author |
Chen, Peng Wong, Limsoon Li, Jinyan |
author_sort |
Chen, Peng |
title |
Detection of outlier residues for improving interface prediction in protein heterocomplexes |
title_short |
Detection of outlier residues for improving interface prediction in protein heterocomplexes |
title_full |
Detection of outlier residues for improving interface prediction in protein heterocomplexes |
title_fullStr |
Detection of outlier residues for improving interface prediction in protein heterocomplexes |
title_full_unstemmed |
Detection of outlier residues for improving interface prediction in protein heterocomplexes |
title_sort |
detection of outlier residues for improving interface prediction in protein heterocomplexes |
publishDate |
2013 |
url |
https://hdl.handle.net/10356/103829 http://hdl.handle.net/10220/16551 |
_version_ |
1681058540647612416 |