Prediction of radiosensitivity and radiocurability using a novel supervised artificial neural network.

ABSTRACT:

Background

Radiotherapy has been widely used to treat various cancers, but its efficacy depends on the individual involved. Traditional gene-based machine-learning models have been widely used to predict radiosensitivity. However, there is still a lack of emerging powerful models, artificial neural networks (ANN), in the practice of gene-based radiosensitivity prediction. In addition, ANN may overfit and learn biologically irrelevant features.

Methods

We developed a novel ANN with Selective Connection based on Gene Patterns (namely ANN-SCGP) to predict radiosensitivity and radiocurability. We creatively used gene patterns (gene similarity or gene interaction information) to control the "on-off" of the first layer of weights, enabling the low-dimensional features to learn the

SUBMITTER: Zeng Z 

PROVIDER: S-EPMC9713966 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature