ArticlePLoS computational biology2019
Computational translation of genomic responses from experimental model systems to humans.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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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.
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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
25 citing papers in PubMed.
- Computational strategies for cross-species knowledge transfer.Nature methods · 2026Review
- Marine Compound-Carpatamide D as a Potential Inhibitor Against TOP2A and Its Mutant D1021Y in Colorectal Cancer: Insights from DFT, MEP and Molecular Dynamics Simulation.Molecular biotechnology · 2025Article
- Across preclinical and clinical platforms, approved and investigational psychiatric drugs share pathways and associate with similar molecular functions.Frontiers in drug discovery · 2025Article
- Cross-Species Modeling Identifies Gene Signatures in Type 2 Diabetes Mouse Models Predictive of Inflammatory and Estrogen Signaling Pathways Associated with Alzheimer's Disease Outcomes in Humans.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2025Article
- Computational strategies for cross-species knowledge transfer and translational biomedicine.ArXiv · 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
- Digital twin mathematical models suggest individualized hemorrhagic shock resuscitation strategies.Communications medicine · 2024Article
- Immunomodulatory Peptides as Vaccine Adjuvants and Antimicrobial Agents.Pharmaceuticals (Basel, Switzerland) · 2024Article
- AutoTransOP: translating omics signatures without orthologue requirements using deep learning.NPJ systems biology and applications · 2024Article
- Joint representation of molecular networks from multiple species improves gene classification.PLoS computational biology · 2024Article
- Comprehensive immunoprofiling and systematic adjuvant comparisons for identifying suitable vaccine: Adjuvant pairings.Human vaccines & immunotherapeutics · 2023Article
- Transcriptomic congruence analysis for evaluating model organisms.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Editorial: Model-informed decision making in the preclinical stages of pharmaceutical research and development.Frontiers in pharmacology · 2023Article
- Toward Elucidating the Human Gut Microbiota-Brain Axis: Molecules, Biochemistry, and Implications for Health and Diseases.Biochemistry · 2022Review
- Mimicking the Natural Basement Membrane for Advanced Tissue Engineering.Biomacromolecules · 2022Review
- Transfer learning of clinical outcomes from preclinical molecular data, principles and perspectives.Briefings in bioinformatics · 2022Article
- Hearing loss and tinnitus: association studies for complex-hearing disorders in mouse and man.Human genetics · 2022Review
- Cross-species transcriptomic signatures predict response to MK2 inhibition in mouse models of chronic inflammation.iScience · 2021Article
- Machine learning applications for therapeutic tasks with genomics data.Patterns (New York, N.Y.) · 2021Review
- Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships.Nature communications · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The high failure rate of therapeutics showing promise in mouse models to translate to patients is a pressing challenge in biomedical science. Though retrospective studies have examined the fidelity of mouse models to their respective human conditions, approaches for prospective translation of insights from mouse models to patients remain relatively unexplored. Here, we develop a semi-supervised learning approach for inference of disease-associated human differentially expressed genes and pathways from mouse model experiments. We examined 36 transcriptomic case studies where comparable phenotypes were available for mouse and human inflammatory diseases and assessed multiple computational approaches for inferring human biology from mouse datasets. We found that semi-supervised training of a neural network identified significantly more true human biological associations than interpreting mouse experiments directly. Evaluating the experimental design of mouse experiments where our model was most successful revealed principles of experimental design that may improve translational performance. Our study shows that when prospectively evaluating biological associations in mouse studies, semi-supervised learning approaches, combining mouse and human data for biological inference, provide the most accurate assessment of human in vivo disease processes. Finally, we proffer a delineation of four categories of model system-to-human "Translation Problems" defined by the resolution and coverage of the datasets available for molecular insight translation and suggest that the task of translating insights from model systems to human disease contexts may be better accomplished by a combination of translation-minded experimental design and computational approaches.
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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.