NDLI: Protein Secondary Structure Prediction Method Based on Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Dzikovska, V. Oreskovic, M. Kalajdziski, S. Trivodaliev, K. Davcev, D.
Copyright Year	2008
Description	Author affiliation: Comput. Sci. Dept., Univ. Sts. Cyril & Methodius, Skopje (Dzikovska, V.; Oreskovic, M.; Kalajdziski, S.; Trivodaliev, K.; Davcev, D.)
Abstract	Protein secondary structure prediction remains an open and important problem in life sciences as a first step towards the crucial tertiary structure prediction. In [3], a protein secondary structure prediction algorithm called PSIPRED presents an innovative approach - feeding the neural network (NN) with a position specific scoring matrix as input data. Starting from this idea, in this paper we propose a method based on breaking down the single first level NN classifier, into three separate ones, for each of the secondary structure elements (SSE) types, in order to achieve greater generalization qualities of the first level classifying algorithm. We also introduce the use of sparsely connected feed-forward NNs, instead of the classic fully interconnected one. This network architecture gains considerable speed improvements (for both the training and the testing part of the algorithm) by omitting the most of the remote units that have the poorest influence on the selected amino acid. The prediction results are encouraging - the predictions are similar to the target PDB data and we achieve better accuracy, compared to the predictions obtained from the original PSIPRED algorithm.
Starting Page	176
Ending Page	179
File Size	173942
Page Count	4
File Format	PDF
ISBN	9781424417476
DOI	10.1109/ICBBE.2008.48
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-05-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Protein engineering Accuracy Neural networks Hidden Markov models Prediction methods Amino acids Prediction algorithms Feedforward systems Information technology
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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