Online Machine Learning Based Predictor for Biological Systems

Predicting the behaviour of biological systems is challenging due to their complex nonlinear nature. This paper applies an online machine learning-based methodology to predict the output of biological systems without a priori knowledge of their dynamical models or training data. The presented online predictor is based on a class of artificial neural networks, called Radial Basis Function (RBF) network. The learning capability and low computational complexity of the presented online predictor makes it a promising approach to be utilized in real-time prediction of biological systems. The satisfactory performance of the presented method is validated by applying it to experimental data from a light induced gene-regulatory network.