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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
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WRAP-training-host-pathogen-protein-predictors-Minhas-2018.pdf - Accepted Version - Requires a PDF viewer. Download (1042Kb) | Preview |
Official URL: http://dx.doi.org/10.1142/S0219720018500142
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 | ||||||||||||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QL Zoology Q Science > QP Physiology |
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| Divisions: | Faculty of 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: |
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| Date of first compliant deposit: | 5 November 2019 | ||||||||||||||||||
| Volume: | 16 | ||||||||||||||||||
| Number: | 04 | ||||||||||||||||||
| Article Number: | 1850014 | ||||||||||||||||||
| DOI: | 10.1142/S0219720018500142 | ||||||||||||||||||
| Status: | Peer Reviewed | ||||||||||||||||||
| Publication Status: | Published | ||||||||||||||||||
| Publisher Statement: | 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 | ||||||||||||||||||
| RIOXX Funder/Project Grant: |
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