Joint triplet loss with semi-hard constraint for data augmentation and disease prediction using gene expression data.

ABSTRACT: The accurate prediction of patients with complex diseases, such as Alzheimer's disease (AD), as well as disease stages, including early- and late-stage cancer, is challenging owing to substantial variability among patients and limited availability of clinical data. Deep metric learning has emerged as a promising approach for addressing these challenges by improving data representation. In this study, we propose a joint triplet loss model with a semi-hard constraint (JTSC) to represent data in a small number of samples. JTSC strictly selects semi-hard samples by switching anchors and positive samples during the learning process in triplet embedding and combines a triplet loss function with an angular loss function. Our results indicate that JTSC significantly improves the number of appropri

SUBMITTER: Chung Y 

PROVIDER: S-EPMC10598120 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature