Prediction of In vitro organogenesis of Bacopa monnieri using artificial neural networks and regression models

This study was conducted to determine if artificial neural networks (ANN) can be used to accurately predict in vitro organogenesis of Bacopa monnieri compared with statistical regression. Prediction models were developed for shoot and root organogenesis (outputs) on two culture media (Murashige and Skoog and Gamborg B5) affected by two explant types (leaf and node) and two cytokinins (6-Benzylaminopurine and Thidiazuron at 1.0, 5.0, and 10.0 mu M levels) with and without the addition of auxin (1-Naphthaleneacetic acid 0.1 mu M) (inputs). Categorical data were encoded in numeric form using one-hot encoding technique. Backpropagation (BP) and Kalman filter (KF) learning algorithms were used to develop nonparametric models between inputs and outputs. Correlations be-tween predicted and observed outputs (validation dataset) were similar in both ANN-BP (R values = 0.77, 0.71, 0.68, and 0.48), and ANN-KF (R values = 0.79, 0.68, 0.75, and 0.49), and were higher than regression (R values = 0.13, 0.48, 0.39, and 0.37) models for shoots and roots from leaf and node explants, respectively. Because ANN models have the ability to interpolate from unseen data, they could be used as an effective tool in accurately predicting the in vitro growth kinetics of Bacopa cultures.