ArticleThe AAPS journal2021
Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities.
Article in The AAPS journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- P300 Event-Related Potentials as Cognitive Biomarkers in Neurological and Neuropsychiatric Disorders: A Systematic Review.Revista de neurologia · 2026Pooled it
- Development of a Novel Machine Learning Method for Estimation of Life-Long Chronic Disease Progression and Its Application to Type 2 Diabetes.Clinical and translational science · 2025Trial
- Pharmacometric modeling and machine learning analyses of prognostic and predictive factors in the JAVELIN Gastric 100 phase III trial of avelumab.CPT: pharmacometrics & systems pharmacology · 2022Trial
- Harnessing Machine Learning for Accelerated Drug Discovery: Opportunities and Unmet Challenges.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Towards accurate artificial intelligence models for strain-level phage-host prediction.Briefings in bioinformatics · 2026Article
- Training the next-generation of biomedical scientists through artificial intelligence-driven education and research in pharmacology and pharmaceutical sciences.Experimental biology and medicine (Maywood, N.J.) · 2026Review
- Review
- Overcoming translational barriers in RNA-protein docking: enhancing computational accuracy for targeted drug discovery.Future medicinal chemistry · 2025Review
- Artificial Intelligence Models and Tools for the Assessment of Drug-Herb Interactions.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Computational Design and Optimization of Multi-Compound Multivesicular Liposomes for Co-Delivery of Traditional Chinese Medicine Compounds.AAPS PharmSciTech · 2025Article
- Future Directions for Quantitative Systems Pharmacology.Handbook of experimental pharmacology · 2025Review
- Impact of covariate model building methods on their clinical relevance evaluation in population pharmacokinetic analyses: comparison of the full model, stepwise covariate model (SCM) and SCM+ approaches.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Explainable machine learning prediction of edema adverse events in patients treated with tepotinib.Clinical and translational science · 2024Article
- Go beyond the limits of genetic algorithm in daily covariate selection practice.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare.Pharmaceutics · 2024Review
- Biomarkers of Arginine Methylation in Diabetic Nephropathy: Novel Insights from Bioinformatics Analysis.Diabetes, metabolic syndrome and obesity : targets and therapy · 2024Article
- Integrating machine learning with pharmacokinetic models: Benefits of scientific machine learning in adding neural networks components to existing PK models.CPT: pharmacometrics & systems pharmacology · 2024Article
- Tumor growth inhibition modeling in patients with second line biliary tract cancer and first line non-small cell lung cancer based on bintrafusp alfa trials.CPT: pharmacometrics & systems pharmacology · 2024Article
- Bridging the Worlds of Pharmacometrics and Machine Learning.Clinical pharmacokinetics · 2023Review
- Modeling tumor size dynamics based on real-world electronic health records and image data in advanced melanoma patients receiving immunotherapy.CPT: pharmacometrics & systems pharmacology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The exponential increase in our ability to harness multi-dimensional biological and clinical data from experimental to real-world settings has transformed pharmaceutical research and development in recent years, with increasing applications of artificial intelligence (AI) and machine learning (ML). Patient-centered iterative forward and reverse translation is at the heart of precision medicine discovery and development across the continuum from target validation to optimization of pharmacotherapy. Integration of advanced analytics into the practice of Translational Medicine is now a fundamental enabler to fully exploit information contained in diverse sources of big data sets such as "omics" data, as illustrated by deep characterizations of the genome, transcriptome, proteome, metabolome, microbiome, and exposome. In this commentary, we provide an overview of ML applications in drug discovery and development, aligned with the three strategic pillars of Translational Medicine (target, patient, dose) and offer perspectives on their potential to transform the science and practice of the discipline. Opportunities for integrating ML approaches into the discipline of Pharmacometrics are discussed and will revolutionize the practice of model-informed drug discovery and development. Finally, we posit that joint efforts of Clinical Pharmacology, Bioinformatics, and Biomarker Technology experts are vital in cross-functional team settings to realize the promise of AI/ML-enabled Translational and Precision Medicine.
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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.