Computational blueprints for cell fate programming.

Pengyi Yang

Abstract readReview

In one paragraph

Review in Stem cell reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

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

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.

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

1 author.

Pengyi YangComputational Systems Biology Unit, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia; School of Mathematics and Statistics, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia; Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia; Charles Perkins Centre, The University of Sydney, Camperdown, NSW 2006, Australia. Electronic address: pengyi.yang@sydney.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cell fate programming enables applications in disease modeling, drug discovery, and regenerative medicine. Foundational studies established differentiation protocols, but their scalability is constrained by combinatorial complexity. Computational methods enable cell annotation, network inference, trajectory analysis, and have been applied to prioritize transcription factors and small molecules for cell fate programming, although prospective adoption for protocol design remains uneven. Single-cell and spatial omics, perturbation screens, and deep learning expand predictive scope while introducing challenges in domain shift, interpretability, and reproducibility. Here, I synthesize these approaches as pragmatic computational blueprints embedded in an iterative design-test-learn pipeline for cell fate programming.

Indexed as

Cell DifferentiationComputational BiologyAnimalsHumansTranscription FactorsTranscription Factors---

Identifiers

PMID42208532

DOI10.1016/j.stemcr.2026.102929

PMCPMC13261891

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

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.