ArticlePatterns (New York, N.Y.)2026
Embeddings from language models are good learners for single-cell data analysis.
Article in Patterns (New York, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Mapping Gene Expression to an Interpretable Semantic Space.bioRxiv : the preprint server for biology · 2026Article
- scGPA: an LLM-assisted workflow for directional virtual gene perturbation analysis from single-cell transcriptomes.BMC genomics · 2026Article
- Cell-o1 : training LLMs to solve single-cell reasoning puzzles with reinforcement learning.Bioinformatics (Oxford, England) · 2026Article
- A survey on large language models in biology and chemistry.Experimental & molecular medicine · 2026Review
- Pre-training genomic language model with variants for better modeling functional genomics.NPJ artificial intelligence · 2026Article
- Scouter predicts transcriptional responses to genetic perturbations with large language model embeddings.Nature computational science · 2026Article
- A perspective on developing foundation models for analyzing spatial transcriptomic data.Quantitative biology (Beijing, China) · 2025Article
- Decoding cell fate: integrated experimental and computational analysis at the single-cell level.Bioinformatics (Oxford, England) · 2025Review
- Pre-training Genomic Language Model with Variants for Better Modeling Functional Genomics.bioRxiv : the preprint server for biology · 2025Article
- sciLaMA: A Single-Cell Representation Learning Framework to Leverage Prior Knowledge from Large Language Models.bioRxiv : the preprint server for biology · 2025Article
- CellFM: a large-scale foundation model pre-trained on transcriptomics of 100 million human cells.Nature communications · 2025Article
- Building a unified model for drug synergy analysis powered by large language models.Nature communications · 2025Article
- Article
- Modeling and predicting single-cell multi-gene perturbation responses with scLAMBDA.bioRxiv : the preprint server for biology · 2024Article
- Foundation models for bioinformatics.Quantitative biology (Beijing, China) · 2024Article
- A mini-review on perturbation modelling across single-cell omic modalities.Computational and structural biotechnology journal · 2024Review
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6 authors.
Funding
Abstract
Foundation models (FMs) have been built to analyze single-cell data with different degrees of success. Here, we present scELMo (single-cell embedding from language models), a method for analyzing single-cell data with the help of large language models (LLMs). LLMs can generate both the description of metadata information and the embeddings for such descriptions. We then combine the embeddings from LLMs with the raw data under the zero-shot learning framework to further extend its function by using the fine-tuning framework to handle different tasks. We demonstrate that scELMo is capable of cell clustering, batch effect correction, and cell-type annotation without training a new model. Moreover, the fine-tuning framework of scELMo can help with more challenging tasks, including
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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.