About this Research Topic
The goal of this Research Topic is to evaluate and discuss the potentials of integrating mechanistic simulation and machine learning in solving increasingly complex physiological and medical questions. As such, we aim to address the following questions:
- What are the challenges of existing modelling frameworks to answer complex, multi-scale physiological and medical questions?
- How can combinatorial computing/ modelling techniques improve the practice of personalized medicine?
- What are possible benefits and limitations of developing hybrid models in biology, bioengineering and biomedicine?
This Research Topic will feature a collection of articles that will be focused on:
• Integration of machine learning and mechanistic models in biomedicine
• Challenges of the hybrid model computing framework
• Multi-scale modelling in areas of physiology, bioengineering and medicine
• Machine learning as surrogate models of complex mechanistic models
• Parameter reduction and uncertainty quantification in machine learning and mechanistic models
Keywords: machine learning, computer simulation, hybrid model, Multi-scale modelling
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.