Paper Info
Title
Automated Structural Classification of Proteins by Using Decision Trees and Structural Protein Features
Abstract
The protein function is tightly related to classification of proteins in hierarchical levels where proteins share same or similar functions. One of the most relevant protein classification schemes is the structural classification of proteins (SCOP). The SCOP scheme has one negative drawback; due to its manual classification methods, the dynamic of classification of new proteins is much slower than the dynamic of discovering novel protein structures in the protein data bank (PDB). In this work, we propose two approaches for automated protein classification. We extract protein descriptors from the structural coordinates stored in the PDB files. Then we apply C4.5 algorithm to select the most appropriate descriptor features for protein classification based on the SCOP hierarchy. We propose novel classification approach by introducing a bottom-up classification flow, and a multi-level classification approach. The results show that these approaches are much faster than other similar algorithms with comparable accuracy.
Year
DOI
Keywords
2009
10.1007/978-3-642-10781-8_15
c4.5 classification,protein function prediction.,structural classification of proteins scop
Field
DocType
Citations 
Decision tree,Pattern recognition,Computer science,Classification scheme,Artificial intelligence,Protein Data Bank,Hierarchy,Structural Classification of Proteins database,Protein Data Bank (RCSB PDB),Protein function prediction,Protein structure
Conference
0
PageRank 
References 
Authors
0.34
7
6
Name
Order
Citations
PageRank
Slobodan Kalajdziski12010.27
Bojan Pepik2875.12
Ilinka Ivanovska300.34
Georgina Mirceva4118.12
Kire Trivodaliev5217.36
Danco Davcev613131.41