ArticleBioinformatics (Oxford, England)2019
Simulation-assisted machine learning.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
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- Simulation-Based Design and Machine Learning Optimization of a Novel Liquid Cooling System for Radio Frequency Coils in Magnetic Hyperthermia.Bioengineering (Basel, Switzerland) · 2025Article
- Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
- Biologically informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post treatment glioblastoma.NPJ digital medicine · 2024Article
- Biologically-informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post-treatment glioblastoma.Research square · 2024Article
- Challenges and opportunities of integrating imaging and mathematical modelling to interrogate biological processes.The international journal of biochemistry & cell biology · 2022Review
- Classification of Non-Functional Requirements From IoT Oriented Healthcare Requirement Document.Frontiers in public health · 2022Article
- Prediction of Tuberculosis Using an Automated Machine Learning Platform for Models Trained on Synthetic Data.Journal of pathology informatics · 2022Article
- Learning from Scarce Information: Using Synthetic Data to Classify Roman Fine Ware Pottery.Entropy (Basel, Switzerland) · 2021Article
- Multiscale modeling meets machine learning: What can we learn?Archives of computational methods in engineering : state of the art reviews · 2021Article
- Online Machine Learning for Accelerating Molecular Dynamics Modeling of Cells.Frontiers in molecular biosciences · 2021Article
- Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities.JMIR medical informatics · 2020Article
- PredictingFrontiers in human neuroscience · 2020Article
- Toward a grey box approach for cardiovascular physiome.The Korean journal of physiology & pharmacology : official journal of the Korean Physiological Society and the Korean Society of Pharmacology · 2019Review
- Integrating machine learning and multiscale modeling-perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences.NPJ digital medicine · 2019Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationIn a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient system details are not known, one typically turns to machine learning, which builds a black-box model of the system using a large dataset of input sample features and outputs. We consider a setting which is between these two extremes: some details of the system mechanics are known but not enough for creating simulations that can be used to make high quality predictions. In this context we propose using approximate simulations to build a kernel for use in kernelized machine learning methods, such as support vector machines. The results of multiple simulations (under various uncertainty scenarios) are used to compute similarity measures between every pair of samples: sample pairs are given a high similarity score if they behave similarly under a wide range of simulation parameters. These similarity values, rather than the original high dimensional feature data, are used to build the kernel.
resultsWe demonstrate and explore the simulation-based kernel (SimKern) concept using four synthetic complex systems-three biologically inspired models and one network flow optimization model. We show that, when the number of training samples is small compared to the number of features, the SimKern approach dominates over no-prior-knowledge methods. This approach should be applicable in all disciplines where predictive models are sought and informative yet approximate simulations are available. AVAILABILITY AND IMPLEMENTATION: The Python SimKern software, the demonstration models (in MATLAB, R), and the datasets are available at https://github.com/davidcraft/SimKern. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.