PGAGP: Predicting pathogenic genes based on adaptive network embedding algorithm.

ABSTRACT: The study of disease-gene associations is an important topic in the field of computational biology. The accumulation of massive amounts of biomedical data provides new possibilities for exploring potential relations between diseases and genes through computational strategy, but how to extract valuable information from the data to predict pathogenic genes accurately and rapidly is currently a challenging and meaningful task. Therefore, we present a novel computational method called PGAGP for inferring potential pathogenic genes based on an adaptive network embedding algorithm. The PGAGP algorithm is to first extract initial features of nodes from a heterogeneous network of diseases and genes efficiently and effectively by Gaussian random projection and then optimize the features of nodes by

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC9895109 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature