ArticleNAR genomics and bioinformatics2026
A systematic assessment of single-cell language model configurations.
Article in NAR genomics and bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- Representation learning of single-cell RNA-seq data.RNA (New York, N.Y.) · 2026Review
- Heimdall: A Modular Framework for Tokenization in Single-Cell Foundation Models.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
3 authors.
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Abstract
Transformers pre-trained on single-cell transcriptomic data have recently been applied to a series of tasks, earning them the title of foundation models. However, recent benchmarks indicate that current iterations of single-cell foundation models may often be surpassed by simpler task-specific models. As all currently published models in this class employ vastly different strategies, it is impossible to determine which practices drive their success (or failure). To help steer research in a more productive direction, we present a framework for the study of single-cell foundation models:
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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.