Predicting clinical information using gene expression is challenging gi ven the complexit y and hi gh dimensi onalit y of gene data. This study propose a deep learning framework for cancer diagnosis through feature extraction and classifier based on various pre-trained autoencoder technologies for kidney cancer. It can be fine-tuned for any tasks and predict clinical information by neural network classifiers. Our model achieved micro and macro F1-scores of 96.2% and 95.8% for gender, 95.8% and 76.3% for race, and 99.8% and 99.6% for sample t ype predi cti ons, respecti ...