ReviewFrontiers in systems biology2024
The rise of scientific machine learning: a perspective on combining mechanistic modelling with machine learning for systems biology.
Review in Frontiers in systems biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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Who cites it
21 citing papers in PubMed.
- Beyond predictive accuracy: A case for mechanism-informed, uncertainty-aware machine learning in food microbiology.iScience · 2026Review
- Modelling African Swine Fever Transmission and Epidemiology: A Scoping Review of Mechanistic, Statistical, and Machine Learning Approaches.Tropical medicine and infectious disease · 2026Review
- Reimagining Lignin Valorization: Synthetic Biology-Enabled Sustainable Aromatic Carbon Biomanufacturing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Protein force spectroscopy using magnetic tweezers: Slow and steady wins the race?Biophysical journal · 2026Review
- Gene dependency-informed inference of response to targeted cancer therapies.Nature communications · 2026Article
- Improvement in model flexibility reveals signaling pathway in T cell responses to pulsatile stimuli.NPJ systems biology and applications · 2026Article
- Mechanistic learning to predict and understand minimal residual disease.bioRxiv : the preprint server for biology · 2026Article
- Integrating AI, mechanistic modelling and network approaches in systems biology for translational research.NPJ systems biology and applications · 2026Article
- Machine-learning-assisted comparative analysis of rice growth and yield formation in field and plant factory systems.Frontiers in plant science · 2026Article
- A hybrid ML-PBPK digital twin framework for clinically interpretable readmission risk and drug exposure stratification in diabetes.Frontiers in digital health · 2026Article
- A review on in-silico analysis of immune cell trafficking and interactions with the tumour microenvironment.Frontiers in oncology · 2026Review
- Productive chaos and precision engineering: decoupling discovery from manufacturing to revolutionize plant-inspired therapeutics.Frontiers in plant science · 2026Article
- The evolution of Alzheimer's target identification: Towards a fusion of artificial and cellular intelligence.The journal of prevention of Alzheimer's disease · 2025Article
- The Endocannabinoid-Microbiota-Neuroimmune Super-System: A Unifying Feedback Architecture for Systems Resilience, Collapse Trajectories, and Precision Feedback Medicine.International journal of molecular sciences · 2025Review
- The Dawn of High-Throughput and Genome-Scale Kinetic Modeling: Recent Advances and Future Directions.ACS synthetic biology · 2025Review
- Understanding and Predicting Population Response to Anthropogenic Disturbance: Current Approaches and Novel Opportunities.Ecology letters · 2025Review
- Jaxkineticmodel: Neural ordinary differential equations inspired parameterization of kinetic models.PLoS computational biology · 2025Article
- The dawn of a new era: can machine learning and large language models reshape QSP modeling?Journal of pharmacokinetics and pharmacodynamics · 2025Review
- From single cells to communities: Mathematical perspectives on bacterial quorum sensing.Computational and structural biotechnology journal · 2025Review
- BADDADAN: Mechanistic modelling of time-series gene module expression.Quantitative plant biology · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Both machine learning and mechanistic modelling approaches have been used independently with great success in systems biology. Machine learning excels in deriving statistical relationships and quantitative prediction from data, while mechanistic modelling is a powerful approach to capture knowledge and infer causal mechanisms underpinning biological phenomena. Importantly, the strengths of one are the weaknesses of the other, which suggests that substantial gains can be made by combining machine learning with mechanistic modelling, a field referred to as Scientific Machine Learning (SciML). In this review we discuss recent advances in combining these two approaches for systems biology, and point out future avenues for its application in the biological sciences.
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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.