ArticleCell research2024
GeneCompass: deciphering universal gene regulatory mechanisms with a knowledge-informed cross-species foundation model.
Article in Cell research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
78 citing papers in PubMed.
- scYeast: a biological-knowledge-guided foundation model on yeast single-cell transcriptomics.Synthetic and systems biotechnology · 2027Article
- Article
- Article
- CompBio and MIRaS-a multi-omic analysis platform built on a memory-based intelligence engine.Nucleic acids research · 2026Article
- Application of human induced pluripotent stem cells for tissue modeling and therapy: are we on track?Stem cells (Dayton, Ohio) · 2026Review
- Deep learning representations of human Immune Health for precision immunology.bioRxiv : the preprint server for biology · 2026Article
- Advancing bioinformatics with language models: components, applications, and perspectives.Briefings in bioinformatics · 2026Review
- scDifformer: diffusion-based post-training for virtual cell modeling across large-scale single-cell data.Nucleic acids research · 2026Article
- Evaluating the role of pretraining dataset size and diversity on single-cell foundation model performance.Nature methods · 2026Article
- PertAdapt: unlocking single-cell foundation models for genetic perturbation prediction via condition-sensitive adaptation.Bioinformatics (Oxford, England) · 2026Article
- Evaluating the learnability of single-cell large language models on multiple tasks.BMC genomics · 2026Article
- Computational modelling of cell identity.The Biochemical journal · 2026Review
- Generalist biological artificial intelligence in modeling the language of life.Nature biotechnology · 2026Review
- Applications of large-scale artificial intelligence models in bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Defining the combinatorial nature of gene modules in prostate cancer underlying lineage plasticity and metastasis.Research square · 2026Article
- CAPTAIN: a multimodal foundation model pretrained on co-assayed single-cell RNA and protein.Nature communications · 2026Article
- Transformers for single-cell RNA sequencing: a survey.Briefings in bioinformatics · 2026Review
- scHILL: deciphering individual-level immune cell heterogeneity with single-cell RNA sequencing data.Briefings in bioinformatics · 2026Article
- Strategies for constructing context-specific protein-protein interaction networks.Briefings in bioinformatics · 2026Review
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
18 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
51 authors.
Funding
Abstract
Deciphering universal gene regulatory mechanisms in diverse organisms holds great potential for advancing our knowledge of fundamental life processes and facilitating clinical applications. However, the traditional research paradigm primarily focuses on individual model organisms and does not integrate various cell types across species. Recent breakthroughs in single-cell sequencing and deep learning techniques present an unprecedented opportunity to address this challenge. In this study, we built an extensive dataset of over 120 million human and mouse single-cell transcriptomes. After data preprocessing, we obtained 101,768,420 single-cell transcriptomes and developed a knowledge-informed cross-species foundation model, named GeneCompass. During pre-training, GeneCompass effectively integrated four types of prior biological knowledge to enhance our understanding of gene regulatory mechanisms in a self-supervised manner. By fine-tuning for multiple downstream tasks, GeneCompass outperformed state-of-the-art models in diverse applications for a single species and unlocked new realms of cross-species biological investigations. We also employed GeneCompass to search for key factors associated with cell fate transition and showed that the predicted candidate genes could successfully induce the differentiation of human embryonic stem cells into the gonadal fate. Overall, GeneCompass demonstrates the advantages of using artificial intelligence technology to decipher universal gene regulatory mechanisms and shows tremendous potential for accelerating the discovery of critical cell fate regulators and candidate drug targets.
Indexed as
Identifiers
What Socratic 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 Socratic graph.