NDLI: Predicting microbial interactions using vector autoregressive model with graph regularization

Content Provider	ACM Digital Library
Author	Hu, Xiaohua Xu, Weiwei Jiang, Xingpeng Park, E. K.
Copyright Year	2015
Abstract	Microbial interactions play important roles on the structure and function of complex microbial communities. With the rapid accumulation of high-throughput metagenomic or 16S rRNA sequencing data, it is possible to infer complex microbial interactions. Co-occurrence patterns of microbial species among multiple samples are often utilized to infer interactions. There are few methods to consider the temporally interacting patterns among microbial species. In this paper, we present a Graph-regularized Vector Autoregressive (GVAR) model to infer causal relationships among microbial entities. The new model has advantage comparing to the original vector autoregressive (VAR) model. Specifically, GVAR can incorporate similarity information for microbial interaction inference--i.e., GVAR assumed that if two species are similar in the previous stage, they tend to have similar influence on the other species in the next stage. We apply the model on a time series dataset of human gut microbiome which was treated with repeated antibiotics. The experimental results indicate that the new approach has better performance than several other VAR-based models and demonstrate its capability of extracting relevant microbial interactions.
Starting Page	254
Ending Page	261
Page Count	8
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2014.2338298
Volume Number	12
Issue Number	2
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-03-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Biological network Gut microbiome Microbial interaction Time series analysis Vector autoregressive model
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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