Published In
Informatics in Medicine Unlocked
Document Type
Article
Publication Date
10-3-2021
Subjects
Neural Networks
Abstract
In this paper, we predict the interaction of proteins between Humans and Yersinia pestis via amino acid sequences. We utilize multiple Natural Language Processing (NLP) methods available in deep learning in a unique format and produce promising results. Our developed model gives a cross-validation AUC score of 0.92 and is comparable with other work that utilizes extensive biochemical properties i.e, network and sequence in conjunction. We achieve this by combining advanced tools in neural machine translation into an integrated end-to-end deep learning framework as well as methods of preprocessing that are novel to the field of bioinformatics. We show that our proposed approach is robust to different protein–protein interactions between host and pathogen data.
Rights
© 2021 The Authors. Published by Elsevier Ltd.
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DOI
10.1016/j.imu.2021.100738
Persistent Identifier
https://archives.pdx.edu/ds/psu/36642
Citation Details
Mathews, N., Tran, T., Rekabdar, B., & Ekenna, C. (2021). Predicting human–pathogen protein–protein interactions using Natural Language Processing methods. Informatics in Medicine Unlocked, 26, 100738.