Random forests algorithm boosts genetic risk prediction of systemic lupus erythematosus.

ABSTRACT: Patients with systemic lupus erythematosus (SLE) present varied clinical manifestations, posing a diagnostic challenge for physicians. Genetic factors substantially contribute to SLE development. A polygenic risk scoring (PRS) model has been used to estimate the genetic risk of SLE in individuals. However, this approach assumes independent and additive contribution of genetic variants to disease development. We aimed to improve the accuracy of SLE prediction using machine-learning algorithms. We applied random forest (RF), support vector machine (SVM), and artificial neural network (ANN) to classify SLE cases and controls using the data from our previous genome-wide association studies (GWAS) conducted in either Chinese or European populations, including a total of 19,208 participants. The

SUBMITTER: Ma W 

PROVIDER: S-EPMC9421562 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature