Deakin University
Browse

File(s) under permanent embargo

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 Zhang
With 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.

History

Pagination

545-551

Location

Washington, D.C.

Start date

2013-09-22

End date

2013-09-25

ISBN-13

9781450324342

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2013, ACM

Editor/Contributor(s)

[Unknown]

Title of proceedings

ACM-BCB 2013 : Proceedings of the 4th ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics

Event

Bioinformatics, Computational Biology and Biomedical Informatics. ACM Conference (4th : 2013 : Washington, D.C.)

Publisher

Association for Computing Machinery

Place of publication

New York, N.Y.