Multi-Omics Analysis and Machine Learning Prediction Model for Pregnancy Outcomes After Intracytoplasmic Sperm Injection-in vitro Fertilization.

ABSTRACT:

Background

To explore the methylation profiles in cumulus cells (CCs) of women undergoing intracytoplasmic sperm injection-in vitro fertilization (ICSI-IVF) and establish a prediction model of pregnancy outcomes using machine learning approaches.

Methods

Methylation data were retrieved from the Gene Expression Omnibus (GEO) database, and differentially methylated genes (DMGs) were subjected to gene set analyses. Support vector machine (SVM), random forest (RF), and logistic regression (LR) were used to establish the prediction model, and microarray data from GEO was analyzed to identify differentially expressed genes (DEGs) associated with the dichotomous outcomes of clinical pregnancy (pregnant vs. non-pregnant). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway

SUBMITTER: Chen F 

PROVIDER: S-EPMC9282825 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature