Construction of a 5-feature gene model by support vector machine for classifying osteoporosis samples.

ABSTRACT: Osteoporosis is a progressive bone disease in the elderly and lacks an effective classification method of patients. This study constructed a gene signature for an accurate prediction and classification of osteoporosis patients. Three gene expression datasets of osteoporosis samples were acquired from the Gene Expression Omnibus database with pre-set criteria. Differentially expressed genes (DEGs) between normal and diseased osteoporosis samples were screened using Limma package in R language. Protein-protein interaction (PPI) network was established based on interaction data of the DEGs from the Human Protein Reference Database. Classification accuracy of the classifier was assessed with sensitivity, specificity and area under curve (AUC) using the pROC package in the R. Pathway enrichment

SUBMITTER: Hu M 

PROVIDER: S-EPMC8806423 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature