Learning vector quantized representation for cancer subtypes identification

Zheng Chen, Ziwei Yang, Lingwei Zhu, Peng Gao, Takashi Matsubara, Shigehiko Kanaya, Md. Altaf-Ul-Amin

Abstract: Highlights•Problem Defining and separating cancer subtypes is essential for facilitating personalized therapy and the prognosis of patients.•What is already known: Existing studies suffer from issues associated with omics data: sample scarcity and high dimensionality.•What this paper adds: A generative vector quantized-variational autoEncode with categorical latent is a new solution.