Evidence map›Paper›PMID 41465103›Full record

ArticleGenes2025

Generation of a Compendium of Transcription Factor Cascades and Identification of Potential Therapeutic Targets Using Graph Machine Learning.

Sonish Sivarajkumar, Romy Roy, Pratyush Tandale, Ankit Bhardwaj, Kipp W Johnson, Anoop Titus, Benjamin S Glicksberg, Shameer Khader, Kamlesh K Yadav, Lakshminarayanan Subramanian

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In one paragraph

Article in Genes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Sonish SivarajkumarMolecular Robotics, Kochi 682022, Kerala, India.ORCID 0000-0003-4173-1988
Romy RoyType 3 Civilization Technologies Pvt Ltd., Thiruvananthapuram 695013, Kerala, India.
Pratyush TandaleHealth Informatics & Data Science, Georgetown University, Washington, DC 20007, USA.
Ankit BhardwajDepartment of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA.
Kipp W JohnsonInstitute for Next Generation Healthcare, Mount Sinai Health System, New York, NY 10065, USA.
Anoop TitusDepartment of Medicine, Brown University, Providence, RI 02912, USA.
Benjamin S GlicksbergHasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.ORCID 0000-0003-4515-8090
Shameer KhaderFaculty of Medicine, Imperial College London, London SW7 2AZ, UK.
Kamlesh K YadavDepartment of Biomedical Sciences, College of Osteopathic Medicine, University of Northern Colorado, Greeley, CO 80639, USA.
Lakshminarayanan SubramanianDepartment of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTranscription factors (TFs) are critical regulators of gene expression, and their dysregulation is implicated in diseases like cancer. This study aims to create a comprehensive resource of TF cascades to identify potential therapeutic targets.

methodsWe extracted TF interactions from the STRING database, constructed a knowledge graph using graph machine learning, and performed pathway enrichment analysis with Enrichr. Network analysis and PageRank identified influential TFs.

resultsWe generated 81,488 unique TF cascades, with the longest containing 62 TFs. Key TFs (e.g., MYC, TP53, STAT3) were identified, and enriched pathways included cancer-related processes. A knowledge graph and dataset were made publicly available.

conclusionsThis compendium of TF cascades provides a valuable resource for understanding TF interactions and identifying novel drug targets for precision therapeutics.

Indexed as

Gene Regulatory NetworksMachine LearningNeoplasmsTranscription FactorsComputational BiologyDatabases, GeneticHumansTranscription Factorscancergraph machine learningknowledge graphpathway enrichmentTF cascadestherapeutic targetstranscription factors

Identifiers

PMID41465103
PMCPMC12733007

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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.