loading page

Hyperbolic Manifold Learning on Differential Expression Signatures
  • Domonkos Pogány ,
  • Péter Antal
Domonkos Pogány
Department of Measurement and Information Systems

Corresponding Author:[email protected]

Author Profile
Péter Antal
Author Profile

Abstract

Our paper contributes to understanding differential expression gene (DEG) signatures, a crucial element in the study of gene expression patterns. One of the key findings of our research, which has not been previously published, is the assertion that the DEG signature space significantly manifests hyperbolic properties. This discovery has far-reaching implications for both the fields of bioinformatics and machine learning, such as drug-target interaction prediction and visualizations for drug discovery.