The rise of scientific machine learning : a perspective on combining mechanistic modelling with machine learning for systems biology

Both machine learning and mechanistic modelling approaches have been used independently with great success in systems biology. Machine learning excels in deriving statistical relationships and quantitative prediction from data, while mechanistic modelling is a powerful approach to capture knowledge and infer causal mechanisms underpinning biological phenomena. Importantly, the strengths of one are the weaknesses of the other, which suggests that substantial gains can be made by combining machine learning with mechanistic modelling, a field referred to as Scientific Machine Learning (SciML). In this review we discuss recent advances in combining these two approaches for systems biology, and point out future avenues for its application in the biological sciences.

Bibliographic Details

Main Authors:	Noordijk, Ben, Garcia Gomez, Monica L., ten Tusscher, Kirsten H.W.J., de Ridder, Dick, van Dijk, Aalt D.J., Smith, Robert W.
Format:	Article/Letter to editor biblioteca
Language:	English
Subjects:	biology-informed neural network (BINN), machine learning, mechanistic models, ordinary differential equations, parameter estimation, scientific machine learning (SciML), system identification,
Online Access:	https://research.wur.nl/en/publications/the-rise-of-scientific-machine-learning-a-perspective-on-combinin
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