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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.

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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, TiffanyORCID iD for 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
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