A review of feature selection methods for machine learning-based disease risk prediction

N Pudjihartono, T Fadason, AW Kempa-Liehr… - Frontiers in …, 2022 - frontiersin.org
Machine learning has shown utility in detecting patterns within large, unstructured, and
complex datasets. One of the promising applications of machine learning is in precision …

Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data

J Yan, SL Risacher, L Shen… - Briefings in bioinformatics, 2018 - academic.oup.com
In the past decade, significant progress has been made in complex disease research across
multiple omics layers from genome, transcriptome and proteome to metabolome. There is an …

Second-generation PLINK: rising to the challenge of larger and richer datasets

CC Chang, CC Chow, LCAM Tellier, S Vattikuti… - …, 2015 - academic.oup.com
Background PLINK 1 is a widely used open-source C/C++ toolset for genome-wide
association studies (GWAS) and research in population genetics. However, the steady …

A compressed variance component mixed model for detecting QTNs and QTN-by-environment and QTN-by-QTN interactions in genome-wide association studies

M Li, YW Zhang, ZC Zhang, Y ** of immune-related loci identifies new SLE risk variants in individuals with Asian ancestry
C Sun, JE Molineros, LL Looger, X Zhou, K Kim… - Nature …, 2016 - nature.com
Systemic lupus erythematosus (SLE) has a strong but incompletely understood genetic
architecture. We conducted an association study with replication in 4,478 SLE cases and …