Assessment of deep learning and transfer learning for cancer prediction based on gene expression data.

ABSTRACT:

Background

Machine learning is now a standard tool for cancer prediction based on gene expression data. However, deep learning is still new for this task, and there is no clear consensus about its performance and utility. Few experimental works have evaluated deep neural networks and compared them with state-of-the-art machine learning. Moreover, their conclusions are not consistent.

Results

We extensively evaluate the deep learning approach on 22 cancer prediction tasks based on gene expression data. We measure the impact of the main hyper-parameters and compare the performances of neural networks with the state-of-the-art. We also investigate the effectiveness of several transfer learning schemes in different experimental setups.

Conclusion

Based on our experimentat

SUBMITTER: Hanczar B 

PROVIDER: S-EPMC9250744 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature