A cost-effective machine learning-based method for preeclampsia risk assessment and driver genes discovery.

ABSTRACT:

Background

The placenta, as a unique exchange organ between mother and fetus, is essential for successful human pregnancy and fetal health. Preeclampsia (PE) caused by placental dysfunction contributes to both maternal and infant morbidity and mortality. Accurate identification of PE patients plays a vital role in the formulation of treatment plans. However, the traditional clinical methods of PE have a high misdiagnosis rate.

Results

Here, we first designed a computational biology method that used single-cell transcriptome (scRNA-seq) of healthy pregnancy (38 wk) and early-onset PE (28-32 wk) to identify pathological cell subpopulations and predict PE risk. Based on machine learning methods and feature selection techniques, we observed that the Tuning ReliefF (TURF) score h

SUBMITTER: Wang H 

PROVIDER: S-EPMC9972636 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature