Hierarchical Gradient-Based Genetic Sampling for Accurate Prediction of Biological Oscillations

TL;DR

This paper introduces HGGS, a hierarchical sampling framework that improves neural network modeling of biological oscillations by effectively selecting samples near oscillation boundaries, leading to more accurate predictions.

Contribution

The paper presents a novel hierarchical gradient-based genetic sampling method that addresses boundary sensitivity and redundancy issues in modeling biological oscillations.

Findings

HGGS outperforms seven sampling methods in four biological systems.

The framework effectively identifies sensitive oscillation boundaries.

Experimental results show improved prediction accuracy.

Abstract

Biological oscillations are periodic changes in various signaling processes crucial for the proper functioning of living organisms. These oscillations are modeled by ordinary differential equations, with coefficient variations leading to diverse periodic behaviors, typically measured by oscillatory frequencies. This paper explores sampling techniques for neural networks to model the relationship between system coefficients and oscillatory frequency. However, the scarcity of oscillations in the vast coefficient space results in many samples exhibiting non-periodic behaviors, and small coefficient changes near oscillation boundaries can significantly alter oscillatory properties. This leads to non-oscillatory bias and boundary sensitivity, making accurate predictions difficult. While existing importance and uncertainty sampling approaches partially mitigate these challenges, they either fail to resolve the sensitivity problem or result in redundant sampling. To address these limitations, we propose the Hierarchical Gradient-based Genetic Sampling (HGGS) framework, which improves the accuracy of neural network predictions for biological oscillations. The first layer, Gradient-based Filtering, extracts sensitive oscillation boundaries and removes redundant non-oscillatory samples, creating a balanced coarse dataset. The second layer, Multigrid Genetic Sampling, utilizes residual information to refine these boundaries and explore new high-residual regions, increasing data diversity for model training. Experimental results demonstrate that HGGS outperforms seven comparative sampling methods across four biological systems, highlighting its effectiveness in enhancing sampling and prediction accuracy.