Machine learning classification models for fetal skeletal development performance prediction using maternal bone metabolic proteins in goats.

ABSTRACT:

Background

In developing countries, maternal undernutrition is the major intrauterine environmental factor contributing to fetal development and adverse pregnancy outcomes. Maternal nutrition restriction (MNR) in gestation has proven to impact overall growth, bone development, and proliferation and metabolism of mesenchymal stem cells in offspring. However, the efficient method for elucidation of fetal bone development performance through maternal bone metabolic biochemical markers remains elusive.

Methods

We adapted goats to elucidate fetal bone development state with maternal serum bone metabolic proteins under malnutrition conditions in mid- and late-gestation stages. We used the experimental data to create 72 datasets by mixing different input features such as one-hot en

SUBMITTER: Liu Y 

PROVIDER: S-EPMC6802673 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature