ArticleiScience2024
Predicting transcription factor activity using prior biological information.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 11 citations in OpenAlex.
- Profiling neoadjuvant therapy response in rectal cancer using meta-analysis of publicly available transcriptomic RNA-seq datasets.Molecular oncology · 2026Article
- A transcription factor regulatory atlas for activity inference and perturbation prediction.Nucleic acids research · 2026Article
- TWIK-1 plays distinct roles in spinal and peripheral sensory circuits controlling mechanical sensitivity and neuropathic hypersensitivity.Signal transduction and targeted therapy · 2026Article
- ExIR enables prioritizing driver and biomarker genes from omics data in a reference free manner.iScience · 2026Article
- Aberrant CD4bioRxiv : the preprint server for biology · 2026Article
- Enhancing KLF15 activity in cardiomyocytes: a novel approach to prevent pathological reprogramming and fibrosis via nuclease-deficient dCas9VPR.Signal transduction and targeted therapy · 2026Article
- Microenvironmental signals combine to induce non-additive molecular and phenotypic responses in mammary epithelial cells.iScience · 2025Article
- CoTF-reg reveals cooperative transcription factors in oligodendrocyte gene regulation using single-cell multi-omics.Communications biology · 2025Article
- ULMnet: inferring physical cell-cell communication networks from scRNAseq data using univariate linear models.Frontiers in immunology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dysregulation of normal transcription factor activity is a common driver of disease. Therefore, the detection of aberrant transcription factor activity is important to understand disease pathogenesis. We have developed Priori, a method to predict transcription factor activity from RNA sequencing data. Priori has two key advantages over existing methods. First, Priori utilizes literature-supported regulatory information to identify transcription factor-target gene relationships. It then applies linear models to determine the impact of transcription factor regulation on the expression of its target genes. Second, results from a third-party benchmarking pipeline reveals that Priori detects aberrant activity from 124 single-gene perturbation experiments with higher sensitivity and specificity than 11 other methods. We applied Priori and other top-performing methods to predict transcription factor activity from two large primary patient datasets. Our work demonstrates that Priori uniquely discovered significant determinants of survival in breast cancer and identified mediators of drug response in leukemia.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.