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A framework for identifying affinity classes of inorganic materials binding peptide sequences
conference contribution
posted on 2013-01-01, 00:00 authored by N Du, M Knecht, P Prasad, M Swihart, Tiffany WalshTiffany Walsh, A ZhangWith the rapid development of bionanotechnology, there has been a growing interest recently in identifying the affinity classes of the inorganic materials binding peptide sequences. However, there are some distinct characteristics of inorganic materials binding sequence data that limit the performance of many widely-used classification methods. In this paper, we propose a novel framework to predict the affinity classes of peptide sequences with respect to an associated inorganic material. We first generate a large set of simulated peptide sequences based on our new amino acid transition matrix, and then the probability of test sequences belonging to a specific affinity class is calculated through solving an objective function. In addition, the objective function is solved through iterative propagation of probability estimates among sequences and sequence clusters. Experimental results on a real inorganic material binding sequence dataset show that the proposed framework is highly effective on identifying the affinity classes of inorganic material binding sequences.
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Bioinformatics, Computational Biology and Biomedical Informatics. ACM Conference (4th : 2013 : Washington, D.C.)Pagination
545 - 551Publisher
Association for Computing MachineryLocation
Washington, D.C.Place of publication
New York, N.Y.Publisher DOI
Start date
2013-09-22End date
2013-09-25ISBN-13
9781450324342Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2013, ACMEditor/Contributor(s)
[Unknown]Title of proceedings
ACM-BCB 2013 : Proceedings of the 4th ACM Conference on Bioinformatics, Computational Biology and Biomedical InformaticsUsage metrics
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