Conference proceeding
Predicting microbial interactions by using network-constrained regularization incorporating covariate coefficients and connection signs
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, 635
01 Nov 2015
Abstract
Conference Title: 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Conference Start Date: 2015, Nov. 9 Conference End Date: 2015, Nov. 12 Conference Location: Washington, DC, USA Network is an exceptional way of depicting biological information. In biology, many different biological processes are represented by network, such as regulatory network, metabolic network and food web. In biology, network is a powerful supplement to the standard numerical data such as profile or count data. By absorbing network information, Vector autoregressive (VAR) model was proved to be an efficient approach to infer dynamic interactions in biological systems. Variants of network-regularized VAR with different penalties or regularization can avoid the problem of over-fitting and provide great potential in high-dimensional time series analysis. In this paper, we develop a novel regularization method for multivariate VAR which incorporates not only network topology but the signs of the network connections. By virtue of coordinate descent, we present a fast implementation for estimating model parameters. We then apply the proposed approach on several time series data sets especially a time series dataset of human gut microbiomes. The experimental results indicate that the new approach has better performance than other VAR-based models.
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Details
- Title
- Predicting microbial interactions by using network-constrained regularization incorporating covariate coefficients and connection signs
- Creators
- Yan WangXiaohua HuXingpeng JiangTingting HeJie Yuan
- Publication Details
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, 635
- Publisher
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Identifiers
- 991019170384604721