Identifying affinity classes of inorganic materials binding sequences via a graph-based model Du, Nan, Knecht, Marc R., Swihart, Mark T., Tang, Zhenghua, Walsh, Tiffany R. and Zhang, Aidong 2015, Identifying affinity classes of inorganic materials binding sequences via a graph-based model, IEEE/ACM transactions on computational biology and bioinformatics, vol. 12, no. 1, pp. 193-204, doi: 10.1109/TCBB.2014.2321158. Attached Files Name Description MIMEType Size Downloads Title Identifying affinity classes of inorganic materials binding sequences via a graph-based model Author(s) Du, Nan Knecht, Marc R. Swihart, Mark T. Tang, Zhenghua Walsh, Tiffany R. orcid.org/0000-0002-0233-9484 Zhang, Aidong Journal name IEEE/ACM transactions on computational biology and bioinformatics Volume number 12 Issue number 1 Start page 193 End page 204 Total pages 12 Publisher Institute of Electrical and Electronics Engineers (IEEE) Place of publication N.Y., N.Y. Publication date 2015 ISSN 1545-5963 Keyword(s) classification Inorganic material peptide sequences Summary Rapid advances in bionanotechnology have recently generated growing interest in identifying peptides that bind to inorganic materials and classifying them based on their inorganic material affinities. However, there are some distinct characteristics of inorganic materials binding sequence data that limit the performance of many widely-used classification methods when applied to this problem. 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 an amino acid transition matrix tailored for the specific inorganic material. Then the probability of test sequences belonging to a specific affinity class is calculated by minimizing an objective function. In addition, the objective function is minimized through iterative propagation of probability estimates among sequences and sequence clusters. Results of computational experiments on two real inorganic material binding sequence data sets show that the proposed framework is highly effective for identifying the affinity classes of inorganic material binding sequences. Moreover, the experiments on the structural classification of proteins (SCOP) data set shows that the proposed framework is general and can be applied to traditional protein sequences. Language eng DOI 10.1109/TCBB.2014.2321158 Field of Research 030304 Physical Chemistry of Materials 060102 Bioinformatics 030603 Colloid and Surface Chemistry Socio Economic Objective 970103 Expanding Knowledge in the Chemical Sciences HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©2015, Institute of Electrical and Electronics Engineers (IEEE) Persistent URL http://hdl.handle.net/10536/DRO/DU:30070678 Document type: Journal Article Collections: Institute for Frontier Materials GTP Research Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Sun, 15 Mar 2015, 21:06:56 EST Sun, 15 Mar 2015, 21:11:10 EST Mon, 16 Mar 2015, 04:00:27 EST Mon, 16 Mar 2015, 07:42:04 EST Tue, 16 Jun 2015, 06:06:30 EST Thu, 19 Nov 2015, 12:47:56 EST Mon, 23 Nov 2015, 10:58:02 EST Filtered Full Citation counts:   Cited 5 times in TR Web of Science Cited 5 times in Scopus Search Google Scholar Access Statistics: 456 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Sun, 15 Mar 2015, 21:11:10 EST

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