ArticleNature biotechnology2025
Multimodal learning enables chat-based exploration of single-cell data.
Article in Nature biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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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
17 citing papers in PubMed.
- MAPA: A Semantic Network Framework for Functional Module Discovery and Interpretation in Multi-Omics Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- ELISA (Embedding-Linked Interactive Single-cell Agent): an interpretable hybrid generative Artificial Intelligence agent for expression-grounded discovery in single-cell genomics.Briefings in bioinformatics · 2026Article
- Single-cell insights into plant growth, adaptation, and evolution.Journal of integrative plant biology · 2026Review
- CellTypeAI: cell annotation for scRNA-seq using local generative-AI.Bioinformatics (Oxford, England) · 2026Article
- mmContext: an open framework for multimodal contrastive learning of omics and text data.Bioinformatics (Oxford, England) · 2026Article
- Substance abuse and inflammation: consequences beyond the brain.Trends in immunology · 2026Review
- NanoCortex: A Unified Agentic System for Nanopore Sequencing Analysis.bioRxiv : the preprint server for biology · 2026Article
- Bio-BLIP: A Multimodal Architecture for Transferable Reasoning in Genomic Variant Interpretation.bioRxiv : the preprint server for biology · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Artificial intelligence agents in cancer research and oncology.Nature reviews. Cancer · 2026Review
- Artificial Intelligence in Transcriptomics: From Human-in-the-Loop to Agentic AI.Journal of personalized medicine · 2026Review
- ChatSpatial: Schema-Enforced Agentic Orchestration for Reproducible and Cross-Platform Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Artificial Intelligence at the Intersection of Chemistry and Materials Science.AI (Basel, Switzerland) · 2026Article
- Let the data speak - single-cell analysis with CellWhisperer.Nature reviews. Genetics · 2026Article
- Scaling Large Language Models for Next-Generation Single-Cell Analysis.bioRxiv : the preprint server for biology · 2026Article
- Efficient and reliable spike sorting from neural recordings with UMAP-based unsupervised nonlinear dimensionality reduction.PLoS biology · 2025Article
- Computational reconstruction of evolutionary selection in human brain networks.Frontiers in neuroinformatics · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Single-cell sequencing characterizes biological samples at unprecedented scale and detail, but data interpretation remains challenging. Here, we present CellWhisperer, an artificial intelligence (AI) model and software tool for chat-based interrogation of gene expression. We establish a multimodal embedding of transcriptomes and their textual annotations, using contrastive learning on 1 million RNA sequencing profiles with AI-curated descriptions. This embedding informs a large language model that answers user-provided questions about cells and genes in natural-language chats. We benchmark CellWhisperer's performance for zero-shot prediction of cell types and other biological annotations and demonstrate its use for biological discovery in a meta-analysis of human embryonic development. We integrate a CellWhisperer chat box with the CELLxGENE browser, allowing users to interactively explore gene expression through a combined graphical and chat interface. In summary, CellWhisperer leverages large community-scale data repositories to connect transcriptomes and text, thereby enabling interactive exploration of single-cell RNA-sequencing data with natural-language chats.
Identifiers
41219484What 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.