Machine Learning Applications for Physics-Based Computational Models of Biological Systems

Modern advances in biomedical imaging, systems biology and multi-scale computational biology, combined with the explosive growth of next generation sequencing data and their analysis using bioinformatics, provide clinicians and life scientists with a dizzying array of information on which to base their decisions. Due to this large data availability, machine learning and, more generally, artificial intelligence techniques for physics-based computational models must increasingly become an integral part of modern biomedical research and industry.

Despite the recent success of machine learning and deep learning in many applications, such as image and speech recognition and natural language processing, other applications from biology and healthcare pose many significantly different challenges to state-of-the-art machine learning methods. Examples include, but are not limited to:

- data heterogeneity and noisiness
- missing values
- multi-rate multi-resolution data nature
- complexity of the underlying physiology
- need for interpretable results
- big and small data
- privacy issues

Therefore, biology and health present some of the most challenging and under-investigated domains of machine learning research to date.

This Research Topic aims to highlight recent efforts to utilize machine learning for modeling biological systems, to address problems across disciplines and to transmit multi-disciplinary ways of thinking. We very much welcome submissions both from machine learning and life science experts, working on developing novel physics-based computational models and methods tailored towards solving complex biological and medical questions by applying machine learning framework.

Keywords: knowledge discovery, explainable models, computational biology and healthcare, physics-based computational models

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.