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TGS

Rapid Reconstruction of Time-Varying Gene Regulatory Networks

v1.0.1 · May 7, 2020 · CC BY-NC-SA 4.0

Description

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.

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dependencies in R code

Namespace in Imports field not imported from: ‘ggm’
  All declared Imports should be used.
NOTE r-devel-linux-x86_64-fedora-gcc

dependencies in R code

Namespace in Imports field not imported from: ‘ggm’
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NOTE 9 OK · 5 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026
NOTE r-devel-linux-x86_64-fedora-clang

dependencies in R code

Namespace in Imports field not imported from: ‘ggm’
  All declared Imports should be used.
NOTE r-devel-linux-x86_64-fedora-gcc

dependencies in R code

Namespace in Imports field not imported from: ‘ggm’
  All declared Imports should be used.
NOTE r-oldrel-macos-arm64

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'LazyData' is specified without a 'data' directory
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'LazyData' is specified without a 'data' directory

Dependency Network

Dependencies Reverse dependencies rjson bnstruct ggm foreach doParallel minet TGS

Version History

3 tracked
new 1.0.1 Mar 10, 2026
updated 1.0.1 ← 1.0.0 diff May 6, 2020
new 1.0.0 Nov 15, 2018