Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142239
Title: Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture
Authors: Le, Nguyen Quoc Khanh
Yapp, Edward Kien Yee
Nagasundaram, Nagarajan
Chua, Matthew Chin Heng
Yeh, Hui-Yuan
Keywords: Humanities::General
Issue Date: 2019
Source: Le, N. Q. K., Yapp, E. K. Y., Nagasundaram, N., Chua, M. C. H., & Yeh, H.-Y. (2019). Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture. Computational and Structural Biotechnology Journal, 17, 1245-1254. doi:10.1016/j.csbj.2019.09.005
Journal: Computational and structural biotechnology journal
Abstract: Protein function prediction is one of the most well-studied topics, attracting attention from countless researchers in the field of computational biology. Implementing deep neural networks that help improve the prediction of protein function, however, is still a major challenge. In this research, we suggested a new strategy that includes gated recurrent units and position-specific scoring matrix profiles to predict vesicular transportation proteins, a biological function of great importance. Although it is difficult to discover its function, our model is able to achieve accuracies of 82.3% and 85.8% in the cross-validation and independent dataset, respectively. We also solve the problem of imbalance in the dataset via tuning class weight in the deep learning model. The results generated showed sensitivity, specificity, MCC, and AUC to have values of 79.2%, 82.9%, 0.52, and 0.861, respectively. Our strategy shows superiority in results on the same dataset against all other state-of-the-art algorithms. In our suggested research, we have suggested a technique for the discovery of more proteins, particularly proteins connected with vesicular transport. In addition, our accomplishment could encourage the use of gated recurrent units architecture in protein function prediction.
URI: https://hdl.handle.net/10356/142239
ISSN: 2001-0370
DOI: 10.1016/j.csbj.2019.09.005
Rights: © 2019 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SoH Journal Articles

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