LIMO-GCN: a linear model-integrated graph convolutional network for predicting Alzheimer disease genes.

ABSTRACT: Alzheimer's disease (AD) is a complex disease with its genetic etiology not fully understood. Gene network-based methods have been proven promising in predicting AD genes. However, existing approaches are limited in their ability to model the nonlinear relationship between networks and disease genes, because (i) any data can be theoretically decomposed into the sum of a linear part and a nonlinear part, (ii) the linear part can be best modeled by a linear model since a nonlinear model is biased and can be easily overfit, and (iii) existing methods do not separate the linear part from the nonlinear part when building the disease gene prediction model. To address the limitation, we propose linear model-integrated graph convolutional network (LIMO-GCN), a generic disease gene prediction metho

SUBMITTER: Lin CX 

PROVIDER: S-EPMC11596108 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature