Mining influential genes based on deep learning.

ABSTRACT:

Background

Currently, large-scale gene expression profiling has been successfully applied to the discovery of functional connections among diseases, genetic perturbation, and drug action. To address the cost of an ever-expanding gene expression profile, a new, low-cost, high-throughput reduced representation expression profiling method called L1000 was proposed, with which one million profiles were produced. Although a set of ~ 1000 carefully chosen landmark genes that can capture ~ 80% of information from the whole genome has been identified for use in L1000, the robustness of using these landmark genes to infer target genes is not satisfactory. Therefore, more efficient computational methods are still needed to deep mine the influential genes in the genome.

Results

Here, w

SUBMITTER: Kong L 

PROVIDER: S-EPMC7821411 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature