Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures. (January 2016)

• Record Type: Journal Article
• Authors: Arana-Daniel, Nancy; Gallegos, Alberto A.; López-Franco, Carlos; Alanís, Alma Y.; Morales, Jacob; López-Franco, Adriana
• Is Part Of: Evolutionary bioinformatics online. Volume 12(2016)
• Publication Date: 2016-01
• Subjects: support vector machines -- evolutionary algorithms -- kernel-adatron -- large scale learning -- machine learning -- protein structure prediction; Bioinformatics -- Periodicals; Evolutionary computation -- Periodicals; Genetic programming (Computer science) -- Periodicals; Computational Biology; Evolution, Molecular; Bioinformatics; Electronic journals; Periodicals; Fulltext; Internet Resources; Periodicals; Periodicals; 576.8
• DOI: 10.4137/EBO.S40912
• Languages: English
• ISSNs: 1176-9343

Abstract

With the increasing power of computers, the amount of data that can be processed in small periods of time has grown exponentially, as has the importance of classifying large-scale data efficiently. Support vector machines have shown good results classifying large amounts of high-dimensional data, such as data generated by protein structure prediction, spam recognition, medical diagnosis, optical character recognition and text classiffication, etc. Most state of the art approaches for large-scale learning use traditional optimization methods, such as quadratic programming or gradient descent, which makes the use of evolutionary algorithms for training support vector machines an area to be explored. The present paper proposes an approach that is simple to implement based on evolutionary algorithms and Kernel-Adatron for solving large-scale classiffication problems, focusing on protein structure prediction. The functional properties of proteins depend upon their three-dimensional structures. Knowing the structures of proteins is crucial for biology and can lead to improvements in areas such as medicine, agriculture and biofuels.