Improving Allergenic Protein Prediction Using Physicochemical Features on Non-Redundant Sequences

Sher Signh, Jr Rou Chiu, Kuei Ling Sun, Emily Chia Yu Su

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

Abstract

Despite extensive studies in allergen prediction, current approaches still have room for performance improvement and suffer from the problem of lack of interpretable biological features. Thus, developments of allergen prediction method from sequences have become highly important to facilitate in silico vaccine design. In this study, we propose a systematic approach to predict allergenic proteins by incorporating sequence and physicochemical properties in machine learning algorithms. In addition, predictive performance of previous studies could be overestimated due to high redundancy in the data sets. Therefore, we reduce sequence redundancy in the data set and experiment results show that we achieve better predictive performance when compared with other approaches. This study can help discover new prophylactic and therapeutic vaccines for diseases. Moreover, we analyze immunological features that can provide valuable insights into immunotherapies of allergy and autoimmune diseases in translational bioinformatics.

Original languageEnglish
Title of host publicationProceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728128160
DOIs
Publication statusPublished - Jul 2019
Event18th International Conference on Machine Learning and Cybernetics, ICMLC 2019 - Kobe, Japan
Duration: Jul 7 2019Jul 10 2019

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
Volume2019-July
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Conference

Conference18th International Conference on Machine Learning and Cybernetics, ICMLC 2019
CountryJapan
CityKobe
Period7/7/197/10/19

Keywords

  • Allergen prediction
  • Machine learning algorithms
  • Physicochemical features
  • Sequence patterns

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Human-Computer Interaction

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  • Cite this

    Signh, S., Chiu, J. R., Sun, K. L., & Su, E. C. Y. (2019). Improving Allergenic Protein Prediction Using Physicochemical Features on Non-Redundant Sequences. In Proceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019 [8949197] (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 2019-July). IEEE Computer Society. https://doi.org/10.1109/ICMLC48188.2019.8949197