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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
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Updated: Jan 13, 2026

Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation
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CellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions.

Guihai Feng1,2,3, Xin Qin4,5, Jiahao Zhang5,6

  • 1State Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|January 7, 2026
PubMed
Summary
This summary is machine-generated.

CellPolaris decodes gene regulatory networks (GRNs) by identifying master transcription factors (TFs) crucial for cell fate. This computational framework aids in understanding developmental processes and simulating TF perturbations.

Keywords:
cell fategene regulatory networkperturbation simulationtransfer learning

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Area of Science:

  • Computational biology
  • Developmental biology
  • Systems biology

Background:

  • Gene regulatory networks (GRNs) control cell fate decisions with precise spatiotemporal gene expression.
  • Accurately capturing context-specific gene regulation, especially with multi-omics data, remains a challenge.

Purpose of the Study:

  • To present CellPolaris, a unified computational framework for decoding transcription factor (TF) roles in development.
  • To enable TF-centered GRN construction, master TF identification, and TF perturbation simulation.

Main Methods:

  • CellPolaris utilizes transfer learning to construct tissue- or cell-type-specific GRNs from pre-existing high-confidence GRNs.
  • The framework requires only transcriptomic data for GRN generation.
  • It identifies master TFs driving cell fate transitions and simulates TF perturbation effects.

Main Results:

  • CellPolaris demonstrates robust performance in GRN construction.
  • Predicted master regulators show significant overlap with experimentally validated TF combinations for cell fate conversion.
  • The framework accurately simulated Rfx2 knockout effects during spermatid differentiation.

Conclusions:

  • CellPolaris provides a comprehensive framework for GRN construction, master TF identification, and perturbation simulation.
  • This tool enhances the elucidation of regulatory mechanisms in developmental processes and cell state transitions.