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Training host-pathogen protein–protein interaction predictors

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Basit, Abdul Hannan, Abbasi, Wajid Arshad, Asif, Amina, Gull, Sadaf and Minhas, Fayyaz ul Amir Afsar (2018) Training host-pathogen protein–protein interaction predictors. Journal of Bioinformatics and Computational Biology, 16 (04). 1850014. doi:10.1142/S0219720018500142 ISSN 0219-7200.

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Official URL: http://dx.doi.org/10.1142/S0219720018500142

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Abstract

Detection of protein–protein interactions (PPIs) plays a vital role in molecular biology. Particularly, pathogenic infections are caused by interactions of host and pathogen proteins. It is important to identify host–pathogen interactions (HPIs) to discover new drugs to counter infectious diseases. Conventional wet lab PPI detection techniques have limitations in terms of cost and large-scale application. Hence, computational approaches are developed to predict PPIs. This study aims to develop machine learning models to predict inter-species PPIs with a special interest in HPIs. Specifically, we focus on seeking answers to three questions that arise while developing an HPI predictor: (1) How should negative training examples be selected? (2) Does assigning sample weights to individual negative examples based on their similarity to positive examples improve generalization performance? and, (3) What should be the size of negative samples as compared to the positive samples during training and evaluation? We compare two available methods for negative sampling: random versus DeNovo sampling and our experiments show that DeNovo sampling offers better accuracy. However, our experiments also show that generalization performance can be improved further by using a soft DeNovo approach that assigns sample weights to negative examples inversely proportional to their similarity to known positive examples during training. Based on our findings, we have also developed an HPI predictor called HOPITOR (Host-Pathogen Interaction Predictor) that can predict interactions between human and viral proteins. The HOPITOR web server can be accessed at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#HoPItor.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Q Science > QL Zoology
Q Science > QP Physiology
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Host-parasite relationships -- Mathematical models, Protein-protein interactions -- Research, Machine learning
Journal or Publication Title: Journal of Bioinformatics and Computational Biology
Publisher: World Scientific Publishing
ISSN: 0219-7200
Official Date: 31 July 2018
Dates:
DateEvent
31 July 2018Published
8 May 2018Accepted
Volume: 16
Number: 04
Article Number: 1850014
DOI: 10.1142/S0219720018500142
Status: Peer Reviewed
Publication Status: Published
Reuse Statement (publisher, data, author rights): Electronic version of an article published as Journal of Bioinformatics and Computational Biology, 16 (04). 1850014 https://doi.org/10.1142/S0219720018500142 © copyright World Scientific Publishing Company https://www.worldscientific.com/worldscinet/jbcb
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 5 November 2019
Date of first compliant Open Access: 5 November 2019
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
FellowshipPakistan Institute of Engineering and Applied Sciences http://www.pieas.edu.pk/
IT & Telecom Endowment FundPakistan Institute of Engineering and Applied Sciences http://www.pieas.edu.pk/
Indigenous 5000 Ph.D. fellowship scheme ; 213-58990-2PS2-046Higher Education Commission, Pakistanhttp://dx.doi.org/10.13039/501100004681
Indigenous 5000 Ph.D. fellowship scheme ; 315-12753-2EG3-197Higher Education Commission, Pakistanhttp://dx.doi.org/10.13039/501100004681
National Research Program for Universities ; 6085Higher Education Commission, Pakistanhttp://dx.doi.org/10.13039/501100004681

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