Utilizing machine learning algorithms to identify biomarkers associated with diabetic nephropathy: A review.

ABSTRACT: Diabetic nephropathy (DN), a multifaceted disease with various contributing factors, presents challenges in understanding its underlying causes. Uncovering biomarkers linked to this condition can shed light on its pathogenesis and support the creation of new diagnostic and treatment methods. Gene expression data were sourced from accessible public databases, and Weighted Gene Co-expression Network Analysis (WGCNA)was employed to pinpoint gene co-expression modules relevant to DN. Subsequently, various machine learning techniques, such as random forest, lasso regression algorithm (LASSO), and support vector machine-recursive feature elimination (SVM-REF), were utilized for distinguishing DN cases from controls using the identified gene modules. Additionally, functional enrichment analyses w

SUBMITTER: Dong B 

PROVIDER: S-EPMC11309603 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature