ArticlePLoS computational biology2020
Systems biology informed deep learning for inferring parameters and hidden dynamics.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers.
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Who cites it
89 citing papers in PubMed.
- A Likelihood-Based Framework for Simultaneously Learning Both Noise and Growth Dynamics Using Biologically-Informed Neural Networks.Bulletin of mathematical biology · 2026Article
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- Selecting synthetic data for successful simulation-based transfer learning in dynamical biological systems.BMC bioinformatics · 2026Article
- dAMN: a genome-scale neural-mechanistic hybrid model to predict bacterial growth dynamics.Bioinformatics (Oxford, England) · 2026Article
- Physics-Informed Machine Learning in Biomedical Science and Engineering.Annual review of biomedical engineering · 2026Review
- A Multiscale Signaling-Biophysical Framework Reveals Mechanisms of Macrophage-Mediated RBC Clearance in Sickle Cell and Gaucher Disease.bioRxiv : the preprint server for biology · 2026Article
- Deep learning for incidence rate prediction and radiation risk assessment of solid tumors.Scientific reports · 2026Article
- Recent Advances in Modeling and Prediction of Blood Glucose in Type 1 Diabetes.Delaware journal of public health · 2026Article
- Patient-specific deep offline artificial pancreas for blood glucose regulation in type 1 diabetes.Smart health (Amsterdam, Netherlands) · 2026Article
- Understanding Iron and Oxidative Stress Response inbioRxiv : the preprint server for biology · 2026Article
- FMEnets: Flow, material, and energy networks for non-ideal plug flow reactor design.Chemical engineering science · 2026Article
- Representation meets optimization: Training PINNs and PIKANs for gray-box discovery in systems pharmacology.Computers in biology and medicine · 2026Article
- Optimized elemental sulfur recovery from sulfide-containing streams applying systems biology and genome-scale metabolic model of Thioalkalivibrio versutus D301.Bioprocess and biosystems engineering · 2026Article
- Navigating the landscape of personalized oncology: overcoming challenges and expanding horizons with computational modeling.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Integrating state-space modeling, parameter estimation, deep learning, and docking techniques in drug repurposing: a case study on COVID-19 cytokine storm.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Fear-driven predator-prey dynamics with prey refuge: analytical framework and physics-informed neural network approach.Frontiers in artificial intelligence · 2026Article
- AI-driven integration and optimization of medicinal plant multi-omics metabolic networks.Frontiers in plant science · 2026Review
29 more citing papers are in PubMed but not listed here.
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4 authors.
Funding
Abstract
Mathematical models of biological reactions at the system-level lead to a set of ordinary differential equations with many unknown parameters that need to be inferred using relatively few experimental measurements. Having a reliable and robust algorithm for parameter inference and prediction of the hidden dynamics has been one of the core subjects in systems biology, and is the focus of this study. We have developed a new systems-biology-informed deep learning algorithm that incorporates the system of ordinary differential equations into the neural networks. Enforcing these equations effectively adds constraints to the optimization procedure that manifests itself as an imposed structure on the observational data. Using few scattered and noisy measurements, we are able to infer the dynamics of unobserved species, external forcing, and the unknown model parameters. We have successfully tested the algorithm for three different benchmark problems.
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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.