Identifying inorganic material affinity classes for peptide sequences based on context learning Xun, Guangxu, Li, Xiaoyi, Knecht, Marc R., Prasad, Paras N., Swihart, Mark T., Walsh, Tiffany and Zhang, Aidong 2015, Identifying inorganic material affinity classes for peptide sequences based on context learning, in BIBM 2015 : Proceedings of the Bioinformatics and Biomedicine 2015 International Conference, IEEE, Piscataway, N.J., pp. 549-554, doi: 10.1109/BIBM.2015.7359742. Attached Files Name Description MIMEType Size Downloads Title Identifying inorganic material affinity classes for peptide sequences based on context learning Author(s) Xun, Guangxu Li, Xiaoyi Knecht, Marc R. Prasad, Paras N. Swihart, Mark T. Walsh, Tiffany orcid.org/0000-0002-0233-9484 Zhang, Aidong Conference name Bioinformatics and Biomedicine. International Conference (2015 : Washington, Distict of Columbia) Conference location Washington, Distict of Columbia Conference dates 9-12 Nov. 2015 Title of proceedings BIBM 2015 : Proceedings of the Bioinformatics and Biomedicine 2015 International Conference Editor(s) [Unknown] Publication date 2015 Conference series Bioinformatics and Biomedicine International Conference Start page 549 End page 554 Total pages 6 Publisher IEEE Place of publication Piscataway, N.J. Keyword(s) Affinity classes Context learning bionanotechnology Summary There is a growing interest in identifying inorganic material affinity classes for peptide sequences due to the development of bionanotechnology and its wide applications. In particular, a selective model capable of learning cross-material affinity patterns can help us design peptide sequences with desired binding selectivity for one inorganic material over another. However, as a newly emerging topic, there are several distinct challenges of it that limit the performance of many existing peptide sequence classification algorithms. In this paper, we propose a novel framework to identify affinity classes for peptide sequences across inorganic materials. After enlarging our dataset by simulating peptide sequences, we use a context learning based method to obtain the vector representation of each amino acid and each peptide sequence. By analyzing the structure and affinity class of each peptide sequence, we are able to capture the semantics of amino acids and peptide sequences in a vector space. At the last step we train our classifier based on these vector features and the heuristic rules. The construction of our models gives us the potential to overcome the challenges of this task and the empirical results show the effectiveness of our models. ISBN 9781467367981 Language eng DOI 10.1109/BIBM.2015.7359742 Field of Research 030304 Physical Chemistry of Materials 030603 Colloid and Surface Chemistry 060102 Bioinformatics Socio Economic Objective 0 Not Applicable HERDC Research category E1 Full written paper - refereed ERA Research output type E Conference publication Grant ID FA9550-12-1-0226 Copyright notice ©2015, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30083037 Document type: Conference Paper Collection: Institute for Frontier Materials Related Links Link Description Connect to Elements publication management system Go to link with your DU access privileges   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 Tue, 26 Apr 2016, 08:43:30 EST Tue, 26 Apr 2016, 08:44:09 EST Tue, 26 Apr 2016, 12:22:10 EST Wed, 27 Apr 2016, 05:00:15 EST Wed, 27 Apr 2016, 06:10:48 EST Thu, 28 Jul 2016, 11:46:08 EST Thu, 28 Jul 2016, 12:12:37 EST Fri, 29 Jul 2016, 06:09:42 EST Mon, 15 Aug 2016, 10:20:48 EST Mon, 15 Aug 2016, 10:27:13 EST Wed, 07 Sep 2016, 11:23:38 EST Wed, 12 Oct 2016, 15:09:14 EST Wed, 09 Nov 2016, 11:10:45 EST Wed, 01 Mar 2017, 07:57:00 EST Filtered Full Citation counts:   Cited 3 times in TR Web of Science Cited 3 times in Scopus Search Google Scholar Access Statistics: 133 Abstract Views, 10 File Downloads  -  Detailed Statistics Created: Tue, 26 Apr 2016, 12:22:10 EST

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