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Prediction of Secondary Structure of Protein Using Support Vector Machine

Published on March 2014 by Shivani Agarwal, Pankaj Agarwal, Deepali Mendiratta
International Conference on Advances in Computer Engineering and Applications
Foundation of Computer Science USA
ICACEA - Number 5
March 2014
Authors: Shivani Agarwal, Pankaj Agarwal, Deepali Mendiratta
87c19259-b00d-4399-bed3-5af4bbd5c48c

Shivani Agarwal, Pankaj Agarwal, Deepali Mendiratta . Prediction of Secondary Structure of Protein Using Support Vector Machine. International Conference on Advances in Computer Engineering and Applications. ICACEA, 5 (March 2014), 1-4.

@article{
author = { Shivani Agarwal, Pankaj Agarwal, Deepali Mendiratta },
title = { Prediction of Secondary Structure of Protein Using Support Vector Machine },
journal = { International Conference on Advances in Computer Engineering and Applications },
issue_date = { March 2014 },
volume = { ICACEA },
number = { 5 },
month = { March },
year = { 2014 },
issn = 0975-8887,
pages = { 1-4 },
numpages = 4,
url = { /proceedings/icacea/number5/15638-1447/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Advances in Computer Engineering and Applications
%A Shivani Agarwal
%A Pankaj Agarwal
%A Deepali Mendiratta
%T Prediction of Secondary Structure of Protein Using Support Vector Machine
%J International Conference on Advances in Computer Engineering and Applications
%@ 0975-8887
%V ICACEA
%N 5
%P 1-4
%D 2014
%I International Journal of Computer Applications
Abstract

The tertiary structure of protein is difficult to predict accurately directly from a protein sequence. The intermediate step is required to predict the structure which project the one dimensional structure into the three dimensional structure. This intermediate step is called secondary structure of protein. The secondary structure of protein plays a key role in the designing of drugs. There are many machine learning algorithms such as HMM (hidden markov model), SVM (Support vector machine), NN (neural network), Fuzzy Logic. A technique which we used to predict the secondary structure of protein is Support Vector Machine (SVM) with Hidden markov transition encoding matrix. Support vector machine is a supervised machine learning method and is based on the principle of the structural risk minimization. The concept of SVM is based on the construction of hyper plane in the high dimensional space to classify the data into the categories. The main objective is to increase the accuracy and decrease the error of prediction.

References
  1. Shivani Agarwal, Arushi Baboota and Deepali Mendiratta 2013. Design and Implementation of an Algorithm to predict Secondary Structure of Proteins using Artificial Neural Network. International Journal of Emerging Research in Management and Technology.
  2. http://crdd. osdd. net/raghava/ccpdb/help. html#regsn
  3. Hae-Jin Hu, Yi Pan. 2004. Improved Protein Secondary Structure Prediction Using Support Vector Machine With a New Encoding Scheme and an Advanced Tertiary Classifier.
  4. Jung-Ying Wang. 2002Application of Support Vector Machines in Bioinformatics.
  5. Jian Guo, Hu Chen, Zhirong Sun, Yuanlie Lin. 2004. A Novel Method for Protein Secondary Structure Prediction Using Dual-Layer SVM and Profiles.
  6. Kasemsant Kuphanumat and Chidchanok Lursinsap. Highly Accurate Protein Secondary Structure Prediction by Combination of nth- order Markov Transition Matrix and Support Vector Machine
  7. Anjum B Reyaz-Ahmed. 2007. Protein Secondary Structure Prediction Using Support Vector Machines, Neural Networks and Genetic Algorithms.
  8. Sujun Hua and Zhirong Sun. 2001. A Novel Method of Protein Secondary Structure Prediction with High Segment Overlap Measure: Support Vector Machine Approach.
  9. Lipontseng Cecilia Tsilo. 2008. Protein Secondary Structure Prediction Using Neural Networks and Support Vector Machines.
Index Terms

Computer Science
Information Sciences

Keywords

Data Set Kernel Function Markov Transition Encoding Scheme Secondary Structure Support Vector Machine.