Predicting Essential Genes of Alzheimer Disease based on Module Partition and Gravity-like Method in Heterogeneous Network

Abstract

The pathogenic mechanism of Alzheimer's disease (AD) is complicated, predicting AD essential genes is an important task in biomedical research, which is helpful in elucidating AD mechanisms and revealing therapeutic targets. In this paper, we propose a random walk algorithm with a restart in the heterogeneous network based on module partition and a gravity-like method (RWRHNMGL) for identifying AD essential genes. The phenotype-gene heterogeneous network (PGHN) is constructed from multiple data sources by considering similar information. These nodes of the optimal module, selected by module partition and covering most functions of AD gene networks, are taken as gene seeds. A refined random walk algorithm is developed to work in the PGHN, the transition matrix is modified by adding a gravity-like method based on subcellular location information, and candidate genes are scored and ranked by a stable probability vector. Finally, the receiver operating characteristic curve (ROC) and Mean Reciprocal Rank is used to evaluate the prediction results of RWRHNMGL. The results show that the RWRHNMGL algorithm performs better in predicting essential genes of AD.