ArticleScientific reports2023
SIGNET: transcriptome-wide causal inference for gene regulatory networks.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Deep learning for psychiatric genomics: from tools to applications.Current opinion in genetics & development · 2026Review
- Extending differential gene expression testing to handle genome aneuploidy in cancer.PLoS computational biology · 2026Article
- From correlation to causation: cell-type-specific gene regulatory networks in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- From Correlation to Causation: Cell-Type-Specific Gene Regulatory Networks in Alzheimer's Disease.bioRxiv : the preprint server for biology · 2025Article
- Transcriptome-wide root causal inference.PLoS computational biology · 2025Article
- DeepAnnotation: A novel interpretable deep learning-based genomic selection model that integrates comprehensive functional annotations.GigaScience · 2025Article
- Transcriptome-Wide Root Causal Inference.medRxiv : the preprint server for health sciences · 2024Article
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6 authors.
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Abstract
Gene regulation plays an important role in understanding the mechanisms of human biology and diseases. However, inferring causal relationships between all genes is challenging due to the large number of genes in the transcriptome. Here, we present SIGNET (Statistical Inference on Gene Regulatory Networks), a flexible software package that reveals networks of causal regulation between genes built upon large-scale transcriptomic and genotypic data at the population level. Like Mendelian randomization, SIGNET uses genotypic variants as natural instrumental variables to establish such causal relationships but constructs a transcriptome-wide gene regulatory network with high confidence. SIGNET makes such a computationally heavy task feasible by deploying a well-designed statistical algorithm over a parallel computing environment. It also provides a user-friendly interface allowing for parameter tuning, efficient parallel computing scheduling, interactive network visualization, and confirmatory results retrieval. The Open source SIGNET software is freely available ( https://www.zstats.org/signet/ ).
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