ArticleiScience2023
Generative pretraining from large-scale transcriptomes for single-cell deciphering.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
What it found
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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.
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Who cites it
43 citing papers in PubMed, 50 citations in OpenAlex.
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- Deep interpretable learning of sample representations for characterizing disease states in single-cell transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Advancing bioinformatics with language models: components, applications, and perspectives.Briefings in bioinformatics · 2026Review
- Computational modelling of cell identity.The Biochemical journal · 2026Review
- Transformers for single-cell RNA sequencing: a survey.Briefings in bioinformatics · 2026Review
- Evaluating the Utilities of Foundation Models in Single-Cell Data Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Scaling and quantization of large-scale foundation model enables resource-efficient predictions in network biology.Nature computational science · 2026Article
- Temporal AI model predicts drivers of cell state trajectories across human aging.bioRxiv : the preprint server for biology · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Representation learning of single-cell RNA-seq data.RNA (New York, N.Y.) · 2026Review
- Artificial intelligence-enabled multi-scale virtual cell: perspective, challenges, and opportunities.Briefings in bioinformatics · 2026Review
- Multimodal foundation models in colorectal cancer: from prediction to trustworthy clinical insight.Briefings in bioinformatics · 2026Review
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- MiRNA Regulations in Cardiotoxicity Induced by Oncologic Therapies and Possible Immune Response.Current medicinal chemistry · 2026Review
- Large-scale multi-omic biosequence transformers for modeling protein-nucleic acid interactions.PloS one · 2026Article
- scDrugMap: benchmarking large foundation models for drug response prediction.Nature communications · 2025Article
- Heimdall: A Modular Framework for Tokenization in Single-Cell Foundation Models.bioRxiv : the preprint server for biology · 2025Article
- Large Language Models for Translational Cancer Informatics.JCO clinical cancer informatics · 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
15 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Exponential accumulation of single-cell transcriptomes poses great challenge for efficient assimilation. Here, we present an approach entitled generative pretraining from transcriptomes (
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What 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.