Integrating Nonlinear Dynamics and Statistical Methods in Deep Learning Models for Biochemical Component Analysis

Abstract

Nonlinear correlations in data that analysis of biological components often requires might be challenging for conventional analytical methods. The intricate nonlinear patterns in biological data could be difficult for conventional biochemical analysis methods to detect, thereby generating less accurate predictions and insights. This disparity highlights the need of more robust analytical techniques able to effectively model and grasp these complexity. We suggest to include nonlinear dynamics with statistical approaches inside deep learning models to raise the accuracy and interpretability of biochemical component analysis. We especially incorporate nonlinear dynamic systems theory into a deep neural network (DNN) architecture to raise the model's potential to identify complex temporal and spatial patterns in biochemical datasets. Embedded into the network design, dynamic system equations are applied in statistical techniques such variance decomposition and regression analysis to enhance model predictions. The proposed method was evaluated on diverse sized biochemical datasets (150,300, 450, and 600 samples). With a 600 sample dataset, the combined DNN model obtained a prediction accuracy of 92.5% compared to 85.7% with conventional techniques. Moreover, the improved model interpretability by 18% found by variance explained in the biochemical component analysis. Including nonlinear dynamics and statistical methods clearly improves the performance and interpretability of biochemical component analysis models, the results demonstrate.