Journal article
Predicting Microbial Interactions Using Vector Autoregressive Model with Graph Regularization
IEEE/ACM transactions on computational biology and bioinformatics, v 12(2), pp 254-261
Mar 2015
PMID: 26357214
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
Metrics
Details
- Title
- Predicting Microbial Interactions Using Vector Autoregressive Model with Graph Regularization
- Creators
- Xingpeng Xingpeng Jiang - Coll. of Comput. & Inf., Drexel Univ., Philadelphia, PA, USAXiaohua Xiaohua Hu - Coll. of Comput. & Inf., Drexel Univ., Philadelphia, PA, USAWeiwei Weiwei Xu - Int. Sch. of Software, Wuhan Univ., Wuhan, ChinaE. K Park - Dept. of Grad. Studies, California State Univ., Chico, CA, USA
- Publication Details
- IEEE/ACM transactions on computational biology and bioinformatics, v 12(2), pp 254-261
- Publisher
- IEEE
- Grant note
- 61170189 / NSFC (10.13039/501100001809) IIP 1160960 / NSF (10.13039/100000001) IIP 1332024 / NNS 90920005 / NSFC (10.13039/501100001809) CCF 0905291 / NSF (10.13039/100000001) 2012BAK24B01 / China National 12-5 plan
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000352788300002
- Scopus ID
- 2-s2.0-84927639466
- Other Identifier
- 991014877770904721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemical Research Methods
- Computer Science, Interdisciplinary Applications
- Mathematics, Interdisciplinary Applications
- Statistics & Probability