Machine learning-based predictive models and drug prediction for schizophrenia in multiple programmed cell death patterns.

ABSTRACT:

Background

Schizophrenia (SC) is one of the most common mental illnesses. However, the underlying genes that cause it and its effective treatments are unknown. Programmed cell death (PCD) is associated with many immune diseases and plays an important role in schizophrenia, which may be a diagnostic indicator of the disease.

Methods

Two groups as training and validation groups were chosen for schizophrenia datasets from the Gene Expression Omnibus Database (GEO). Furthermore, the PCD-related genes of the 12 patterns were extracted from databases such as KEGG. Limma analysis was performed for differentially expressed genes (DEG) identification and functional enrichment analysis. Machine learning was employed to identify minimum absolute contractions and select operator (LASSO)

SUBMITTER: Feng Y 

PROVIDER: S-EPMC10042291 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature