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Title: Inference of biological networks using bi-directional random forest granger causality
Authors: Mohammad Shaheryar Furqan
Mohammad Yakoob Siyal
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Brain Connectivity
Biological Network
Issue Date: 2016
Source: Mohammad Shaheryar Furqan & Mohammad Yakoob Siyal (2016). Inference of biological networks using bi-directional random forest granger causality. SpringerPlus, 5, 514-. doi:10.1186/s40064-016-2156-y
Series/Report no.: SpringerPlus
Abstract: The standard ordinary least squares based Granger causality is one of the widely used methods for detecting causal interactions between time series data. However, recent developments in technology limit the utilization of some existing implementations due to the availability of high dimensional data. In this paper, we are proposing a technique called Bi-directional Random Forest Granger causality. This technique uses the random forest regularization together with the idea of reusing the time series data by reversing the time stamp to extract more causal information. We have demonstrated the effectiveness of our proposed method by applying it to simulated data and then applied it to two real biological datasets, i.e., fMRI and HeLa cell. fMRI data was used to map brain network involved in deductive reasoning while HeLa cell dataset was used to map gene network involved in cancer.
ISSN: 2193-1801
DOI: 10.1186/s40064-016-2156-y
Rights: © 2016 Furqan and Siyal. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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