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SIGNET: Transcriptome-wide Causal Inference for Gene Regulatory Networks
Zhongli Jiang1, Chen Chen2,3, Zhenyu Xu1,3
1Department of Statistics, Purdue University, West Lafayette, 47907, Indiana, United States.
Research Square
|August 7, 2023
Summary
SIGNET is a new software tool that maps gene regulatory networks. It uses genetic data to identify causal gene relationships, advancing human biology and disease research.
Area of Science:
- Genomics
- Systems Biology
- Bioinformatics
Background:
- Gene regulation is crucial for understanding human biology and disease.
- Inferring causal gene relationships from transcriptomic data is computationally challenging.
- Existing methods struggle with the scale of whole-transcriptome analysis.
Conclusions:
- SIGNET offers a powerful and accessible tool for gene regulatory network inference.
- The software facilitates a deeper understanding of gene regulation in human biology and disease.
- SIGNET is freely available as open-source software.
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