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Article

CauFinder: Steering Cell-State and Phenotype Transitions by Causal Disentanglement Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) - 1 Sept 2026

Zhang Chengming, Chen Zexi, Miao Yuanxiang, Shen Zuolin, Cai Deyu, Tang Shijie, Xue Yun, Guo Weifeng, Ji Hongbin, Liu Jian, Aihara Kazuyuki, Chen Luonan

Abstract excerpt

Understanding and controlling cell-state and phenotype transitions is central to biological discovery and therapeutic development, yet identifying true causal regulators from observational transcriptomic data remains challenging because of confounding and correlated signals. CauFinder is a framework that integrates causal disentanglement modeling with network control to prioritize causal regulators of state and...

Read the complete abstract on PubMed

Topics

Humans
Phenotype
Transcriptome
Gene Expression Profiling

Source

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Open in PubMed Open full text

Identifiers

PMID: 42301228
DOI: 10.1002/advs.76177
PMCID: PMC13336959

Genes

DAAM1
EGFR

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 24 Oct 2026.

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