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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(6): 791-793 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.238

Classification of Protein 3D Structures Using Artificial Neural Network

Hui Li, Chunmei Liu, Legand Burge, and William Southerland
Abstract—Three dimensional (3D) protein structures determine the function of a protein within a cell. Classification of 3D structure of proteins is therefore crucial to inferring protein functional information as well as the evolution of interactions between proteins. In this paper, we utilized the artificial neural network (ANN) paradigm to classify the protein structures. The approach equally divides a 3D protein structure into several parts and then extracts statistical features from each part. The different parts in spatial domain are thus viewed as the feature vectors which are input into the neural network. The computational experiments achieve better results in most cases than other currently existing approaches.

Index Terms—Protein structure; classification; ANN.

Hui Li, Chunmei Liu, and Legand Burge are with the Department of Systems and Computer Science, Howard University, Washington, DC 20059, USA (e-mail: hli@scs.howard.edu; chunmei@scs.howard.edu; blegand@scs.howard.edu.).
William Southerland is with the Department of Biochemistry and Molecular Biology Howard University, Washington, DC 20059, USA (email: wsoutherland@fac.howard.edu).

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Cite:Hui Li, Chunmei Liu, Legand Burge, and William Southerland, "Classification of Protein 3D Structures Using Artificial Neural Network," International Journal of Machine Learning and Computing vol.2, no. 6, pp. 791-793, 2012.

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