Mutual Information Based on Multiple Level Discretization Network Inference from Time Series Gene Expression Profiles

Abstract

Discovering a genetic regulatory network (GRN) from time series gene expression data plays an essential role in the field of biomedical research. In its development, many methods have been proposed for inferring GRNs. Although most of them are effective, they have limitations in terms of network size and the number of regulatory genes due to high computational cost. Thus, it is necessary to develop an efficient method that can operate with large networks and provide reliable results within an acceptable run time. In this study, we propose a new method using mutual information based on multi-level discretization network inference (MIDNI) from time series gene expression profiles. The proposed method discretizes time series gene expression data to minimize information loss and computational consumption through K-means clustering. We do not fix the number of clusters, instead varying it depending on the distribution of gene expression values. We compared MIDNI with three well-known inference methods through extensive simulations on both artificial and real gene expression datasets. Our results illustrate that MIDNI significantly outperforms the alternatives in terms of dynamic accuracy. The proposed method represents an efficient and scalable tool for inferring GRNs from time series gene expression data.