Item Details

title

Fourier-enhanced Neural Networks for Systems Biology Applications

author

Xu, Enze

abstract

In the field of systems biology, differential equations are commonly used to model biological systems, but solving them for large-scale and complex systems can be computationally expensive. Recently, the integration of machine learning and mathematical modeling has offered new opportunities for scientific discoveries in biology and health.The emerging physics-informed neural network (PINN) has been proposed as a solution to this problem. However, PINN can be computationally expensive and unreliable for complex biological systems. To address these issues, we propose the Fourier-enhanced Neural Networks for systems biology (SB-FNN). SB-FNN uses an embedded Fourier neural network with an adaptive activation function and a cyclic penalty function to optimize the prediction of biological dynamics, particularly for biological systems that exhibit oscillatory patterns. Experimental results demonstrate that SB-FNN achieves better performance and is more efficient than PINN for handling complex biological models. Experimental results on cellular and population models demonstrate that SB-FNN outperforms PINN in both accuracy and efficiency, making it a promising alternative approach for handling complex biological models. The proposed method achieved better performance on six biological models and is expected to replace PINN as the most advanced method in systems biology.

subject

Adaptive Activation Function

Differential Equations

Fourier Neural Operator

Physics-informed Neural Network

Scientific Machine Learning

Systems Biology

contributor

Chen, Minghan (advisor)

Turkett, William (committee member)

Khuri, Natalia (committee member)

date

2023-06-07T08:35:44Z (accessioned)

2023 (issued)

degree

Computer Science (discipline)

embargo

2025-06-06 (terms)

2025-06-06 (liftdate)

identifier

http://hdl.handle.net/10339/102111 (uri)

language

en (iso)

publisher

Wake Forest University

type

Thesis