Comparing biological information contained in mRNA and non-coding RNAs for classification of lung cancer patients.

ABSTRACT:

Background

Deciphering the meaning of the human DNA is an outstanding goal which would revolutionize medicine and our way for treating diseases. In recent years, non-coding RNAs have attracted much attention and shown to be functional in part. Yet the importance of these RNAs especially for higher biological functions remains under investigation.

Methods

In this paper, we analyze RNA-seq data, including non-coding and protein coding RNAs, from lung adenocarcinoma patients, a histologic subtype of non-small-cell lung cancer, with deep learning neural networks and other state-of-the-art classification methods. The purpose of our paper is three-fold. First, we compare the classification performance of different versions of deep belief networks with SVMs, decision trees and rand

SUBMITTER: Smolander J 

PROVIDER: S-EPMC6892207 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature