Taewan Goo

Abstract Polygenic risk scores (PRSs) quantify an individual’s genetic susceptibility to complex traits and diseases. Conventional PRSs, which are based on linear models, perform poorly for phenotypes with skewed distributions or with genetic effects that vary across the distribution. We propose a quantile regression-based PRS (QPRS) that can capture quantile-specific genetic effects.

Home Full-text access for editors Deep learning-based classification and interpretation of gene expression data from cancer and normal tissues by TaeJin Ahn; Taewan Goo; Chan-Hee Lee; SungMin Kim; Kyullhee Han; Sangick Park; Taesung Park International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 24, No. 2, 2020 Abstract: Outstanding performance has been achieved in resolving recognition and classification problems with deep learning technology.