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Detection of outlier residues for improving interface prediction in protein heterocomplexes

หน่วยงาน Nanyang Technological University, Singapore

รายละเอียด

ชื่อเรื่อง : Detection of outlier residues for improving interface prediction in protein heterocomplexes
นักวิจัย : Chen, Peng , Wong, Limsoon , Li, Jinyan
คำค้น : DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences
หน่วยงาน : Nanyang Technological University, Singapore
ผู้ร่วมงาน : -
ปีพิมพ์ : 2555
อ้างอิง : 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 , http://hdl.handle.net/10220/16551 , http://dx.doi.org/10.1109/TCBB.2012.58
ที่มา : -
ความเชี่ยวชาญ : -
ความสัมพันธ์ : IEEE/ACM transactions on computational biology and bioinformatics
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

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.

บรรณานุกรม :
Chen, Peng , Wong, Limsoon , Li, Jinyan . (2555). Detection of outlier residues for improving interface prediction in protein heterocomplexes.
    กรุงเทพมหานคร : Nanyang Technological University, Singapore.
Chen, Peng , Wong, Limsoon , Li, Jinyan . 2555. "Detection of outlier residues for improving interface prediction in protein heterocomplexes".
    กรุงเทพมหานคร : Nanyang Technological University, Singapore.
Chen, Peng , Wong, Limsoon , Li, Jinyan . "Detection of outlier residues for improving interface prediction in protein heterocomplexes."
    กรุงเทพมหานคร : Nanyang Technological University, Singapore, 2555. Print.
Chen, Peng , Wong, Limsoon , Li, Jinyan . Detection of outlier residues for improving interface prediction in protein heterocomplexes. กรุงเทพมหานคร : Nanyang Technological University, Singapore; 2555.