Aligned deep neural network for integrative analysis with high-dimensional input.

ABSTRACT:

Objective

Deep neural network (DNN) techniques have demonstrated significant advantages over regression and some other techniques. In recent studies, DNN-based analysis has been conducted on data with high-dimensional input such as omics measurements. In such analysis, regularization, in particular penalization, has been applied to regularize estimation and distinguish relevant input variables from irrelevant ones. A unique challenge arises from the "lack of information" attributable to high dimensionality of input and limited size of training data. For many data/studies, there exist other data/studies that may be relevant and can potentially provide additional information to boost performance.

Methods

In this study, we conduct integrative analysis of multiple independent da

SUBMITTER: Zhang S 

PROVIDER: S-EPMC10534141 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature