Deep Autoencoder based Classification for Clinical Prediction of Kidney Cancer

Abstract

Predicting clinical information using gene expression is challenging given the complexity and high dimensionality 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 type predictions, respectively, which is much higher than the values of traditional dimensionality reduction and machine learning techniques. In the results, the conditional variational mutation autoencoder (CVAE) improved the macro F1 score, a difficult race prediction task, by 7.6%. Our results are useful for the prognosis as well as prevention and early diagnosis of kidney cancer.

[한국어 초록 없음]