Identifying inorganic material affinity classes for peptide sequences based on context learning

Guangxu Xun, Xiaoyi Li, Marc Knecht, Paras N. Prasad, Mark T. Swihart, Tiffany R. Walsh, Aidong Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages549-554
Number of pages6
ISBN (Print)9781467367981
DOIs
StatePublished - Dec 16 2015
EventIEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 - Washington, United States
Duration: Nov 9 2015Nov 12 2015

Other

OtherIEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
CountryUnited States
CityWashington
Period11/9/1511/12/15

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ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Health Informatics
  • Biomedical Engineering

Cite this

Xun, G., Li, X., Knecht, M., Prasad, P. N., Swihart, M. T., Walsh, T. R., & Zhang, A. (2015). Identifying inorganic material affinity classes for peptide sequences based on context learning. In Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 (pp. 549-554). [7359742] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2015.7359742