Euiseong Ko (Computer Science), Farhad Shokoohi (Mathematical Sciences), and Mingon Kang (Computer Science) published original research titled "SPIN: sex-specific and pathway-based interpretable neural network for sexual dimorphism analysis" in Briefings in Bioinformatics (IF: 9.5). The study proposes a new deep learning-based unified framework, named SPIN, for sexual dimorphism analysis. They demonstrate that SPIN significantly improved the C-index up to 23.6% in TCGA cancer datasets, and it was further validated using asthma datasets. SPIN identifies sex-specific and -shared risk loci that are often missed in previous sex-combined/-separate analysis. This study was supported by CMS and NSF MRI.