Multiscale Modeling Meets Machine Learning: What Can We Learn?

Submitted by gpeng on

Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable.

The Bond Graph/Cell ML Approach to Formulating Physically Based Models

Submitted by McCullochA on

Edited version of MSM Webinar on bond graphs and CellML for multi-physics modeling originally given by Dr. Peter Hunter  at the University of Auckland on 30 November, 2018.

This tutorial summarizes a unifying approach to developing physically consistent and reproducible models of multi-physics problems over multiple scales of physical organization using markup languages and the theory of bond graphs first proposed by Henry Paynter

BioGears Conference

The BioGears conference will provide the community of medical modeling and simulation professionals a place to discuss advancing the current state-of-the-art in physiological modeling. Conference admission will be free to all who register.

This conference will initiate discussions on how to develop and extend BioGears physiology models for new users and use cases, and will expand the body of knowledge regarding the use of simulated physiology for medical education.

Innovation Lab: Advancing Cancer Biology at the Frontiers of Machine Learning and Mechanistic Models

The National Cancer Institute and Carnegie Mellon University are hosting an Innovation Lab to explore opportunities to combine advances in Artificial Intelligence with the mechanistic modeling approaches of cancer systems biology on June 1-5, 2020. Independent investigators with an interest in combining data driven and mechanistic modeling approaches to cancer biology and expertise i

Application Deadline March 20 - Innovation Lab: Advancing Cancer Biology at the Frontiers of Machine Learning and Mechanistic Models

The @theNCI, @CarnegieMellon, and @knowinnovation are hosting an #InnovationLab to explore the intersection of cancer systems biology, mathematical modeling, and machine learning on June 1-5, 2020. Applications due by March 20th: https://tinyurl.com/IL2020-NCI
Subscribe to