BioinformaticsbioRxivPreprint
G-S-M: A Comprehensive Framework for Integrative Feature Selection in Omics Data Analysis and Beyond
Malik Yousef, Jens Allmer, Yasin İnal, Burcu Bakır-Güngör
The paper introduces G-S-M, a framework for integrative feature selection that combines machine learning and prior knowledge to identify discriminative feature groups for medical applications.
15 citations