Gene Swin transformer: new deep learning method for colorectal cancer prognosis using transcriptomic data.

ABSTRACT: Transcriptome sequencing has become essential in clinical tumor research, providing in-depth insights into the biology and functionality of tumor cells. However, the vast amount of data generated and the complex relationships between gene expressions make it challenging to effectively identify clinically relevant information. In this study, we developed a method called Gene Swin Transformer to address these challenges. This approach converts transcriptomic data into Synthetic Image Elements (SIEs). We utilized data from 12 datasets, including GSE17536-GSE103479 datasets (n = 1771) and The Cancer Genome Atlas (n = 459), to generate SIEs. These elements were then classified based on survival time using deep learning algorithms to predict colorectal cancer prognosis and build a reliable progn

SUBMITTER: Wang Y 

PROVIDER: S-EPMC12165829 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature