Rapid advancements in high-throughput gene sequencing technologies have resulted in genome-scale time-series datasets. Uncovering the underlying temporal sequence of gene regulatory events in the form of time-varying gene regulatory networks demands accurate and computationally efficient algorithms. Such an algorithm is 'TGS'. It is proposed in Saptarshi Pyne, Alok Ranjan Kumar, and Ashish Anand; Rapid reconstruction of time-varying gene regulatory networks; IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(1):278{291, Jan-Feb 2020. The TGS algorithm is shown to consume only 29 minutes for a microarray dataset with 4028 genes. This package provides an implementation of the TGS algorithm and its variants.
Package details |
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Bioconductor views | GeneExpression GraphAndNetwork Microarray Network NetworkInference Software SystemsBiology |
Maintainer | |
License | CC BY-NC-SA 4.0 |
Version | 1.0.1.9000 |
URL | https://www.biorxiv.org/content/early/2018/06/14/272484 https://github.com/sap01/TGS |
Package repository | View on GitHub |
Installation |
Install the latest version of this package by entering the following in R:
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