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Multitask learning for host-pathogen protein interactions
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Kshirsagar, M., Carbonell, J. and Klein-Seetharaman, Judith (2013) Multitask learning for host-pathogen protein interactions. Bioinformatics, Volume 29 (Number 13). i217-i226. doi:10.1093/bioinformatics/btt245
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Official URL: http://dx.doi.org/10.1093/bioinformatics/btt245
Abstract
Motivation: An important aspect of infectious disease research involves understanding the differences and commonalities in the infection mechanisms underlying various diseases. Systems biology-based approaches study infectious diseases by analyzing the interactions between the host species and the pathogen organisms. This work aims to combine the knowledge from experimental studies of host–pathogen interactions in several diseases to build stronger predictive models. Our approach is based on a formalism from machine learning called ‘multitask learning’, which considers the problem of building models across tasks that are related to each other. A ‘task’ in our scenario is the set of host–pathogen protein interactions involved in one disease. To integrate interactions from several tasks (i.e. diseases), our method exploits the similarity in the infection process across the diseases. In particular, we use the biological hypothesis that similar pathogens target the same critical biological processes in the host, in defining a common structure across the tasks.
Results: Our current work on host–pathogen protein interaction prediction focuses on human as the host, and four bacterial species as pathogens. The multitask learning technique we develop uses a task-based regularization approach. We find that the resulting optimization problem is a difference of convex (DC) functions. To optimize, we implement a Convex–Concave procedure-based algorithm. We compare our integrative approach to baseline methods that build models on a single host–pathogen protein interaction dataset. Our results show that our approach outperforms the baselines on the training data. We further analyze the protein interaction predictions generated by the models, and find some interesting insights.
| Item Type: | Journal Article | ||||
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| Divisions: | Faculty of Medicine > Warwick Medical School > Biomedical Sciences > Translational & Experimental Medicine > Metabolic and Vascular Health (- until July 2016) Faculty of Medicine > Warwick Medical School Faculty of Medicine > Warwick Medical School > Biomedical Sciences > Translational & Experimental Medicine |
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| Journal or Publication Title: | Bioinformatics | ||||
| Publisher: | Oxford University Press | ||||
| ISSN: | 1367-4803 | ||||
| Official Date: | 1 July 2013 | ||||
| Dates: |
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| Volume: | Volume 29 | ||||
| Number: | Number 13 | ||||
| Page Range: | i217-i226 | ||||
| DOI: | 10.1093/bioinformatics/btt245 | ||||
| Status: | Peer Reviewed | ||||
| Publication Status: | Published | ||||
| Access rights to Published version: | Restricted or Subscription Access |
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