ArticleArchives of computational methods in engineering : state of the art reviews2021
Multiscale modeling meets machine learning: What can we learn?
Article in Archives of computational methods in engineering : state of the art reviews, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 1 of them a synthesis that pooled it.
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.
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
73 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Revolutionizing personalized medicine using artificial intelligence: a meta-analysis of predictive diagnostics and their impacts on drug development.Clinical and experimental medicine · 2025Pooled it
- Applying a Pharmacometrics-Enabled Machine Learning Analysis to Predict 2-Month Culture Conversion Using Phase 2a Data in a Tuberculosis Clinical Trial.Clinical and translational science · 2026Trial
- Capturing the Multiscale Nature of Bone Behavior: Classical, Data-Driven and Hybrid Techniques.Annals of biomedical engineering · 2026Review
- Molecular Access Engineering for Microbial Biocatalysis: from Enzyme Tunnels to Microbial Cell Factories.Journal of microbiology and biotechnology · 2026Review
- Predictive modeling in biology and medicine: Digital twins and multi-scale modeling.PLoS computational biology · 2026Article
- Simulation of the Metabolic Response to an Interventional Study with New Healthy Beverages by Machine-Learning Regression.Journal of agricultural and food chemistry · 2026Article
- A Framework Integrating Spiking Cortical Circuit Modeling and Simulation-Based Inference to Probe Biomarkers of Cortical Dysfunction in Alzheimer's Disease.Interdisciplinary sciences, computational life sciences · 2026Article
- Rapid prediction of cardiac activation in the left ventricle with geometric deep learning: a step towards cardiac resynchronization therapy planning.NPJ digital medicine · 2026Article
- Inversion of nitrogen content in apple leaves using an explainable PSO-CNN model.Frontiers in plant science · 2026Article
- Body mass index mediates the association between heavy metal exposure and overactive bladder risk: insights from a nationally representative cross-sectional study using explainable machine learning.Translational andrology and urology · 2025Article
- Single-cell multi-omics uncovers CPS1 as a breast cancer immune evasion therapeutic target.Scientific reports · 2025Article
- Comprehensive Proteomics and Machine Learning Analysis to Distinguish Follicular Adenoma and Follicular Thyroid Carcinoma from Indeterminate Thyroid Nodules.Endocrinology and metabolism (Seoul, Korea) · 2025Article
- How multi-scale modeling can help examine social determinants of health and resulting disparities.PLoS computational biology · 2025Article
- Medical digital twins: enabling precision medicine and medical artificial intelligence.The Lancet. Digital health · 2025Review
- A Python toolbox for neural circuit parameter inference.NPJ systems biology and applications · 2025Article
- Assessment of uncertainty quantification in universal differential equations.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Article
- Longitudinal study of COPD phenotypes using integrated SPECT and qCT imaging.Frontiers in physiology · 2025Article
- Personalised Transdermal Therapy for Chronic Pain with Digital Twin Technology.Current drug targets · 2025Review
- Democratizing biomedical simulation through automated model discovery and a universal material subroutine.Computational mechanics · 2025Article
13 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
12 authors.
Funding
Abstract
Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.
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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.