Optimizing artificial neural network models for metabolomics and systems biology: an example using HPLC retention index data

Abstract

Background: Artificial Neural Networks (ANN) are extensively used to model ‘omics’ data. Different modeling methodologies and combinations of adjustable parameters influence model performance and complicate model optimization. Methodology: We evaluated optimization of four ANN modeling parameters (learning rate annealing, stopping criteria, data split method, network architecture) using retention index (RI) data for 390 compounds. Models were assessed by independent validation (I-Val) using newly measured RI values for 1492 compounds. Conclusion: The best model demonstrated an I-Val standard error of 55 RI units and was built using a Ward's clustering data split and a minimally nonlinear network architecture. Use of validation statistics for stopping and final model selection resulted in better independent validation performance than the use of test set statistics.