Biological interpretation of deep neural network for phenotype prediction based on gene expression.

ABSTRACT:

Background

The use of predictive gene signatures to assist clinical decision is becoming more and more important. Deep learning has a huge potential in the prediction of phenotype from gene expression profiles. However, neural networks are viewed as black boxes, where accurate predictions are provided without any explanation. The requirements for these models to become interpretable are increasing, especially in the medical field.

Results

We focus on explaining the predictions of a deep neural network model built from gene expression data. The most important neurons and genes influencing the predictions are identified and linked to biological knowledge. Our experiments on cancer prediction show that: (1) deep learning approach outperforms classical machine learning methods o

SUBMITTER: Hanczar B 

PROVIDER: S-EPMC7643315 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature