A microorganism transcriptional regulation algorithm based on generalized regression neural network

Considering the importance of operon in microorganism transcriptional regulation, this paper sets up a new operon prediction model based on artificial neural network (ANN). Specifically, multiple genome information, ranging from intergenic distance (IGD), orthologous protein cluster (OPC), conserved gene pair (CGP) to system evolution spectrum (SES), were preprocessed by log-likelihood fraction and wavelet transform, and then inputted to the GRNN for operon prediction. The experimental results in E. coli K-12 and B. subtilis 168 show that our model is a valid and feasible way to predict operon. The research findings shed new light on the prediction of operon information of new species. © 2019 by the authors.