Explainable deep transfer learning model for disease risk prediction using high-dimensional genomic data.

ABSTRACT: Building an accurate disease risk prediction model is an essential step in the modern quest for precision medicine. While high-dimensional genomic data provides valuable data resources for the investigations of disease risk, their huge amount of noise and complex relationships between predictors and outcomes have brought tremendous analytical challenges. Deep learning model is the state-of-the-art methods for many prediction tasks, and it is a promising framework for the analysis of genomic data. However, deep learning models generally suffer from the curse of dimensionality and the lack of biological interpretability, both of which have greatly limited their applications. In this work, we have developed a deep neural network (DNN) based prediction modeling framework. We first proposed a g

SUBMITTER: Liu L 

PROVIDER: S-EPMC9328574 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature