Gene Network Analysis of Alzheimer's Disease Based on Network and Statistical Methods

Gene network associated with Alzheimer's disease (AD) is constructed from multiple data sources by considering gene co-expression and other factors. The AD gene network is divided into modules by Cluster one, Markov Clustering (MCL), Community Clustering (Glay) and Molecular Complex Detection (MCODE). Then these division methods are evaluated by network structure entropy, and optimal division method, MCODE. Through functional enrichment analysis, the functional module is identified. Furthermore, we use network topology properties to predict essential genes. In addition, the logical regression algorithm under Bayesian framework is used to predict essential genes of AD. Based on network pharmacology, four kinds of AD's herb-active compounds-active compound targets network and AD common core network are visualized, then the better herbs and herb compounds of AD are selected through enrichment analysis.

Automated summary

A gene network associated with Alzheimer's disease is constructed from multiple data sources, divided into modules and evaluated using different methods. Functional modules are identified through enrichment analysis, and essential genes are predicted using network topology properties and a logical regression algorithm under a Bayesian framework. Based on network pharmacology, potential herbs and herb compounds for AD are selected through visualization and enrichment analysis.