Protein Fold Classification Based on Machine Learning Paradigm - A Review

Katarzyna STĄPOR

doi:10.2478/bams-2012-0003

Protein Fold Classification Based on Machine Learning Paradigm - A Review

Katarzyna STĄPOR

Department of Applied Informatics (RAU/RAU7)

Research output: Contribution to journal › Article › peer-review

Abstract

Protein fold recognition using machine learning-based methods is crucial in the protein structure discovery, especially when the traditional sequence comparison methods fail because the structurally-similar proteins share little in the way of sequence homology. Many different machine learning-based fold classification methods have been proposed with still increasing accuracy and the main aim of this article is to cover all the major results in this field.

Original language	English
Pages (from-to)	53
Number of pages	1
Journal	Bio-Algorithms and Med-Systems
Volume	8
Issue number	1
DOIs	https://doi.org/10.2478/bams-2012-0003
Publication status	Published - 2012

Keywords

classifier
features
protein fold recognition
supervised learning algorithm

ASJC Scopus subject areas

General Computer Science
Medicine (miscellaneous)
Biochemistry, Genetics and Molecular Biology (miscellaneous)
Health Informatics

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10.2478/bams-2012-0003

Cite this

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title = "Protein Fold Classification Based on Machine Learning Paradigm - A Review",

abstract = "Protein fold recognition using machine learning-based methods is crucial in the protein structure discovery, especially when the traditional sequence comparison methods fail because the structurally-similar proteins share little in the way of sequence homology. Many different machine learning-based fold classification methods have been proposed with still increasing accuracy and the main aim of this article is to cover all the major results in this field.",

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AB - Protein fold recognition using machine learning-based methods is crucial in the protein structure discovery, especially when the traditional sequence comparison methods fail because the structurally-similar proteins share little in the way of sequence homology. Many different machine learning-based fold classification methods have been proposed with still increasing accuracy and the main aim of this article is to cover all the major results in this field.

KW - classifier

KW - features

KW - protein fold recognition

KW - supervised learning algorithm

UR - https://www.scopus.com/pages/publications/85025258243

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DO - 10.2478/bams-2012-0003

M3 - Article

AN - SCOPUS:85025258243

SN - 1895-9091

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JO - Bio-Algorithms and Med-Systems

JF - Bio-Algorithms and Med-Systems

IS - 1

ER -

Protein Fold Classification Based on Machine Learning Paradigm - A Review

Abstract

Keywords

ASJC Scopus subject areas

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