Contrastive Learning for Predicting Cancer Prognosis Using Gene Expression Values

arXiv:2306.06276v3 Announce Type: replace
Abstract: Recent advancements in image classification have demonstrated that contrastive learning (CL) can aid in further learning tasks by acquiring good feature representation from a limited number of data samples. In this paper, we applied CL to tumor transcriptomes and clinical data to learn feature representations in a low-dimensional space. We then utilized these learned features to train a classifier to categorize tumors into a high- or low-risk group of recurrence. Using data from The Cancer …

abstract arxiv cancer classification clinical cs.lg data feature gene good image learn paper representation samples tasks type values