ArticleMethods in molecular biology (Clifton, N.J.)2026
A Deep Learning Approach to Assessing Cell Identity in Stem Cell-Based Embryo Models.
Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- A novel paradigm for single-cell annotation in stem cell research.Stem cell reports · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since the generation of embryoid bodies from embryonic stem cells (ESCs), three-dimensional differentiation has been used to mimic developmental processes. To what extent do these in vitro cell types reflect the cells generated by the embryo? We used deep learning (DL) to develop an integrated model of early human development leveraging existing single-cell RNA-seq (scRNA-seq) and using scvi-tools to both integrate and classify cell types. This tool can interrogate in vitro cell types and assign them both identity and provide an entropy score for the reliability of this classification. In this protocol we explain how to use state-of-the-art tools and our associated, publicly available DL models for early embryonic development to explore phenotypes and cell types derived in vitro. Our tools represent an important new resource to interrogate stem cell-based embryo models and the fidelity with which they recapitulate development.
Indexed as
Identifiers
40690128What OpenQuestion holds
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.