NDLI: Automatic protein structure classification through structural fingerprinting

Content Provider	IEEE Xplore Digital Library
Author	Zeyar Aung Kian-Lee Tan
Copyright Year	2004
Description	Author affiliation: Dept. of Comput. Sci., Nat. Univ. of Singapore, Singapore (Zeyar Aung; Kian-Lee Tan)
Abstract	In this paper, we present a new scheme named "CP-Mine" for automatic three-dimensional (3D) protein structure classification using structural fingerprints. We represent a 3D protein structure as a CPset, which is a set of inter-SSE contact patterns (CPs) existing in the protein. Suppose we have a database of protein structures whose class labels are already known, and suppose there are distinct protein structure classes in the database. For each class, we generate its fingerprint by mining the frequent CPsets from all the member protein structures belonging to this class. When we want to predict the class label of an unknown protein, we also generate the CPset of this protein, and find the intersection between this CPset and the fingerprint of each protein structure class one by one. Then, the labels of the classes with the highest degree of intersection are returned as the answer. The proposed method is a pure classification scheme in that any kind of structural comparison, alignment or searching is not needed to be performed. The preliminary experimental results shows that our method can classify the protein structures accurately and efficiently.
Starting Page	508
Ending Page	515
File Size	472069
Page Count	8
File Format	PDF
ISBN	0769521738
DOI	10.1109/BIBE.2004.1317385
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-05-21
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fingerprint recognition Spatial databases Data mining Bioinformatics Predictive models Computer science Drives Laboratories Protein engineering Machine learning
Content Type	Text
Resource Type	Article

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