ArticleNature methods2018
Found In Translation: a machine learning model for mouse-to-human inference.
Article in Nature methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integrative transcriptomics and hypothesis-driven transfer machine learning reveal conserved and species-specific host-parasite dynamics acrossFrontiers in cellular and infection microbiology · 2026Pooled it
- Systems biology framework for the rational design of operational conditions for in vitro/in vivo translation of tissue models.Science advances · 2026Article
- A survey of transformer and LLM-based architectures, workflows, and systems in drug discovery.Journal of computer-aided molecular design · 2026Review
- Navigating AI and machine learning in cancer research: an end-to-end translational framework.Journal of translational medicine · 2026Review
- Deep learning of 777 K bulk transcriptomes reveals human-mouse gene conservation beyond DNA sequence similarity.Communications biology · 2026Article
- Computational translation of mouse models of osteoarthritis predicts human disease.Osteoarthritis and cartilage · 2026Article
- singIST: An integrative method for comparative single-cell transcriptomics between disease models and humans.PLoS computational biology · 2026Article
- Computational strategies for cross-species knowledge transfer.Nature methods · 2026Review
- singIST: an R/bioconductor library and quarto dashboard for automated single-cell comparative transcriptomics analysis of disease models and humans.Frontiers in genetics · 2026Article
- Cross-species blood transcriptional correlates of BCG-mediated protection against tuberculosis include innate and adaptive immune processes.JCI insight · 2025Article
- Computational Translation of Mouse Models of Osteoarthritis Predicts Human Disease.bioRxiv : the preprint server for biology · 2025Article
- Cross-species transcriptomics translation reveals a role for the unfolded protein response in Mycobacterium tuberculosis infection.NPJ systems biology and applications · 2025Article
- Ginkgolide B increases healthspan and lifespan of female mice.Nature aging · 2025Article
- Mitochondrial fatty acid oxidation regulates monocytic type I interferon signaling via histone acetylation.Science advances · 2025Article
- Biomarker discovery using machine learning in the psychosis spectrum.Biomarkers in neuropsychiatry · 2024Article
- Computational strategies for cross-species knowledge transfer and translational biomedicine.ArXiv · 2024Article
- Improving the predictive power of mouse models.Nature biotechnology · 2024Article
- Systematic Analysis of Human Colorectal Cancer scRNA-seq Revealed Limited Pro-tumoral IL-17 Production Potential in Gamma Delta T Cells.bioRxiv : the preprint server for biology · 2024Article
- Drug targeting in psychiatric disorders - how to overcome the loss in translation?Nature reviews. Drug discovery · 2024Review
- Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Cross-species differences form barriers to translational research that ultimately hinder the success of clinical trials, yet knowledge of species differences has yet to be systematically incorporated in the interpretation of animal models. Here we present Found In Translation (FIT; http://www.mouse2man.org ), a statistical methodology that leverages public gene expression data to extrapolate the results of a new mouse experiment to expression changes in the equivalent human condition. We applied FIT to data from mouse models of 28 different human diseases and identified experimental conditions in which FIT predictions outperformed direct cross-species extrapolation from mouse results, increasing the overlap of differentially expressed genes by 20-50%. FIT predicted novel disease-associated genes, an example of which we validated experimentally. FIT highlights signals that may otherwise be missed and reduces false leads, with no experimental cost.
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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.