Deep learning approach for cancer subtype classification using high-dimensional gene expression data.

ABSTRACT:

Motivation

Studies have shown that classifying cancer subtypes can provide valuable information for a range of cancer research, from aetiology and tumour biology to prognosis and personalized treatment. Current methods usually adopt gene expression data to perform cancer subtype classification. However, cancer samples are scarce, and the high-dimensional features of their gene expression data are too sparse to allow most methods to achieve desirable classification results.

Results

In this paper, we propose a deep learning approach by combining a convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU): our approach, DCGN, aims to achieve nonlinear dimensionality reduction and learn features to eliminate irrelevant factors in gene expression data. Spe

SUBMITTER: Shen J 

PROVIDER: S-EPMC9575247 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature