TY - JOUR
T1 - The rise of scientific machine learning
T2 - a perspective on combining mechanistic modelling with machine learning for systems biology
AU - Noordijk, Ben
AU - Garcia Gomez, Monica L.
AU - ten Tusscher, Kirsten H.W.J.
AU - de Ridder, Dick
AU - van Dijk, Aalt D.J.
AU - Smith, Robert W.
PY - 2024/8/2
Y1 - 2024/8/2
N2 - 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.
AB - 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.
KW - biology-informed neural network (BINN)
KW - machine learning
KW - mechanistic models
KW - ordinary differential equations
KW - parameter estimation
KW - scientific machine learning (SciML)
KW - system identification
U2 - 10.3389/fsysb.2024.1407994
DO - 10.3389/fsysb.2024.1407994
M3 - Article
AN - SCOPUS:85201417161
SN - 2674-0702
VL - 4
JO - Frontiers in Systems Biology
JF - Frontiers in Systems Biology
M1 - 1407994
ER -