Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150972
Title: Using word embedding technique to efficiently represent protein sequences for identifying substrate specificities of transporters
Authors: Nguyen, Trinh-Trung-Duong
Le, Nguyen Quoc Khanh
Ho, Quang-Thai
Phan, Dinh-Van
Ou, Yu-Yen
Keywords: Science::Biological sciences
Issue Date: 2019
Source: Nguyen, T., Le, N. Q. K., Ho, Q., Phan, D. & Ou, Y. (2019). Using word embedding technique to efficiently represent protein sequences for identifying substrate specificities of transporters. Analytical Biochemistry, 577, 73-81. https://dx.doi.org/10.1016/j.ab.2019.04.011
Journal: Analytical Biochemistry
Abstract: Membrane transport proteins and their substrate specificities play crucial roles in various cellular functions. Identifying the substrate specificities of membrane transport proteins is closely related to protein-target interaction prediction, drug design, membrane recruitment, and dysregulation analysis, thus being an important problem for bioinformatics researchers. In this study, we applied word embedding approach, the main cause for natural language processing breakout in recent years, to protein sequences of transporters. We defined each protein sequence based on the word embeddings and frequencies of its biological words. The protein features were then fed into machine learning models for prediction. We also varied the lengths of protein sequence's constituent biological words to find the optimal length which generated the most discriminative feature set. Compared to four other feature types created from protein sequences, our proposed features can help prediction models yield superior performance. Our best models reach an average area under the curve of 0.96 and 0.99, respectively on the 5-fold cross validation and the independent test. With this result, our study can help biologists identify transporters based on substrate specificities as well as provides a basis for further research that enriches a field of applying natural language processing techniques in bioinformatics.
URI: https://hdl.handle.net/10356/150972
ISSN: 0003-2697
DOI: 10.1016/j.ab.2019.04.011
Rights: © 2019 Elsevier Inc. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SoH Journal Articles

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