A GASVM Algorithm for Predicting Protein Structure Classes - Journal of Computer and Communications

JCC > Vol.4 No.15, November 2016

Journal of Computer and Communications

Volume 4, Issue 15 (November 2016)

ISSN Print: 2327-5219 ISSN Online: 2327-5227

Google-based Impact Factor: 1.12 Citations

A GASVM Algorithm for Predicting Protein Structure Classes ()

HTML XML

Download as PDF (Size: 334KB) PP. 46-53

DOI: 10.4236/jcc.2016.415004 1,319 Downloads 2,417 Views Citations

Author(s)

Longlong Liu, Mingjiao Ma^*, Tingting Zhao

Affiliation(s)

Department of Mathematical Sciences, Ocean University of China, Qingdao, China.

ABSTRACT

The research methods of protein structure prediction mainly focus on finding effective features of protein sequences and developing suitable machine learning algorithms. But few people consider the importance of weights of features in classification. We propose the GASVM algorithm (classification accuracy of support vector machine is regarded as the fitness value of genetic algorithm) to optimize the coefficients of these 16 features (5 features are proposed first time) in the classification, and further develop a new feature vector. Finally, based on the new feature vector, this paper uses support vector machine and 10-fold cross-validation to classify the protein structure of 3 low similarity datasets (25PDB, 1189, FC699). Experimental results show that the overall classification accuracy of the new method is better than other methods.

KEYWORDS

Protein Structural Classes, Protein Secondary Structure, Genetic Algorithm, Support Vector Machine

Share and Cite:

Liu, L. , Ma, M. and Zhao, T. (2016) A GASVM Algorithm for Predicting Protein Structure Classes. Journal of Computer and Communications, 4, 46-53. doi: 10.4236/jcc.2016.415004.

Cited by

[1]	Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms
	2021

[2]	Predicting structural class for protein sequences of 40% identity based on features of primary and secondary structure using Random Forest algorithm
	2019

Journals Menu

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

Journals Menu

Home

About SCIRP

Service

Policies