ArticlePatterns (New York, N.Y.)2025
BioLLM: A standardized framework for integrating and benchmarking single-cell foundation models.
Article in Patterns (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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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
15 citing papers in PubMed.
- Benchmarking biomedical foundation models.Nature methods · 2026Review
- Foundation models in omics research: a comprehensive survey.Briefings in bioinformatics · 2026Review
- Clonal Metamorphosis: Deconstructing MPN Evolution with Single-Cell and Spatial Multi-Omics.Clinical and experimental medicine · 2026Review
- Article
- Evaluating the learnability of single-cell large language models on multiple tasks.BMC genomics · 2026Article
- Full-Body AI Agent: A Perspective on Multi-Scale Collaborative AI for Systemic Biology and Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Generalist biological artificial intelligence in modeling the language of life.Nature biotechnology · 2026Review
- Network methods for diagonal integration of unpaired single-cell multiomics data: a review.Bioinformatics (Oxford, England) · 2026Review
- RegFormer: a single-cell foundation model powered by gene regulatory hierarchies.Nature communications · 2026Article
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 2026Review
- A unified framework enables accessible deployment and comprehensive benchmarking of single-cell foundation models.bioRxiv : the preprint server for biology · 2026Article
- Editorial: Artificial intelligence in multi-omics: advancing tumor metastasis prediction and mechanism analysis.Frontiers in cell and developmental biology · 2026Article
- Overcoming barriers to the wide adoption of single-cell large language models in biomedical research.Nature biotechnology · 2025Article
- Single-cell foundation models: bringing artificial intelligence into cell biology.Experimental & molecular medicine · 2025Review
- A Multi-Layered Framework for Modeling Human Biology: From Basic AI Agents to a Full-Body AI Agent.ArXiv · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application and evaluation of single-cell foundation models (scFMs) present significant challenges due to heterogeneous architectures and coding standards. To address this, we introduce BioLLM (biological large language model), a unified framework for integrating and applying scFMs to single-cell RNA sequencing analysis. BioLLM provides a unified interface that integrates diverse scFMs, eliminating architectural and coding inconsistencies to enable streamlined model access. With standardized APIs and comprehensive documentation, BioLLM supports streamlined model switching and consistent benchmarking. Our comprehensive evaluation of scFMs revealed distinct strengths and limitations, highlighting scGPT's robust performance across all tasks, including zero shot and fine-tuning. Geneformer and scFoundation demonstrated strong capabilities in gene-level tasks, benefiting from effective pretraining strategies. In contrast, scBERT lagged behind, likely due to its smaller model size and limited training data. Ultimately, BioLLM aims to empower the scientific community to leverage the full potential of foundational models, advancing our understanding of complex biological systems through enhanced single-cell analysis.
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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.