Analysis of RNA-Seq data using self-supervised learning for vital status prediction of colorectal cancer patients.

ABSTRACT:

Background

RNA sequencing (RNA-Seq) is a technique that utilises the capabilities of next-generation sequencing to study a cellular transcriptome i.e., to determine the amount of RNA at a given time for a given biological sample. The advancement of RNA-Seq technology has resulted in a large volume of gene expression data for analysis.

Results

Our computational model (built on top of TabNet) is first pretrained on an unlabelled dataset of multiple types of adenomas and adenocarcinomas and later fine-tuned on the labelled dataset, showing promising results in the context of the estimation of the vital status of colorectal cancer patients. We achieve a final cross-validated (ROC-AUC) Score of 0.88 by using multiple modalities of data.

Conclusion

The results of this study

SUBMITTER: Padegal G 

PROVIDER: S-EPMC10249191 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature