ReviewFrontiers in immunology2024
A review of mechanistic learning in mathematical oncology.
Review in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
What it found
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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
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Correlation does not equal causation: the imperative of causal inference in machine learning models for immunotherapy.Frontiers in immunology · 2025Pooled it
- Topologically-based parameter inference for agent-based model selection from spatiotemporal cellular data.PLoS computational biology · 2026Article
- Computational Oncology of Chemotaxis-Driven Tumour-Immune Spatial Patterning and Stability.Bioengineering (Basel, Switzerland) · 2026Article
- Advancing Tumor Treatment Through Artificial Intelligence and Mathematical Modeling: A Comprehensive Review.Health science reports · 2026Article
- In silico models in oncology, neurology, and epidemiology: systems-level and multiscale perspectives.NPJ systems biology and applications · 2026Review
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
- TumorTwin: a Python framework for patient-specific digital twins in oncology.BMC medical informatics and decision making · 2026Article
- Mechanistic learning to predict and understand minimal residual disease.bioRxiv : the preprint server for biology · 2026Article
- Digital Twin models to address long-term treatment toxicities in children and young adults with cancer.NPJ digital medicine · 2026Review
- Physics-Informed Machine Learning in Biomedical Science and Engineering.Annual review of biomedical engineering · 2026Review
- Experimentally calibrated multiscale model predicts schedule dependent drug combination effects.NPJ systems biology and applications · 2026Article
- Who's afraid of synthetic data? Hybrid approaches to deliver medical digital twins.Informatics in medicine unlocked · 2026Article
- The future of mathematical oncology in the age of AI.NPJ systems biology and applications · 2026Review
- From mechanistic models to artificial intelligence: exploring the potential of digital twins in geriatric oncology.Frontiers in artificial intelligence · 2026Article
- A review on in-silico analysis of immune cell trafficking and interactions with the tumour microenvironment.Frontiers in oncology · 2026Review
- Mathematical Oncology: How Modeling Is Transforming Clinical Decision-Making.Cancer research · 2025Review
- Advances in surrogate modeling for biological agent-based simulations: trends, challenges, and future prospects.Journal of mathematical biology · 2025Review
- Data-driven identification of biological systems using multi-scale analysis.PLoS computational biology · 2025Article
- Article
- Acidic Tumor Microenvironments and Emerging Therapeutic Strategies for Cancer Therapy.Yonsei medical journal · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mechanistic learning refers to the synergistic combination of mechanistic mathematical modeling and data-driven machine or deep learning. This emerging field finds increasing applications in (mathematical) oncology. This review aims to capture the current state of the field and provides a perspective on how mechanistic learning may progress in the oncology domain. We highlight the synergistic potential of mechanistic learning and point out similarities and differences between purely data-driven and mechanistic approaches concerning model complexity, data requirements, outputs generated, and interpretability of the algorithms and their results. Four categories of mechanistic learning (sequential, parallel, extrinsic, intrinsic) of mechanistic learning are presented with specific examples. We discuss a range of techniques including physics-informed neural networks, surrogate model learning, and digital twins. Example applications address complex problems predominantly from the domain of oncology research such as longitudinal tumor response predictions or time-to-event modeling. As the field of mechanistic learning advances, we aim for this review and proposed categorization framework to foster additional collaboration between the data- and knowledge-driven modeling fields. Further collaboration will help address difficult issues in oncology such as limited data availability, requirements of model transparency, and complex input data which are embraced in a mechanistic learning framework.
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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.