Artificial intelligence and the analysis of multi-platform metabolomics data for the detection of intrauterine growth restriction.

ABSTRACT:

Objective

To interrogate the pathogenesis of intrauterine growth restriction (IUGR) and apply Artificial Intelligence (AI) techniques to multi-platform i.e. nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) based metabolomic analysis for the prediction of IUGR.

Materials and methods

MS and NMR based metabolomic analysis were performed on cord blood serum from 40 IUGR (birth weight < 10th percentile) cases and 40 controls. Three variable selection algorithms namely: Correlation-based feature selection (CFS), Partial least squares regression (PLS) and Learning Vector Quantization (LVQ) were tested for their diagnostic performance. For each selected set of metabolites and the panel consists of metabolites common in three selection algorithms so-called ove

SUBMITTER: Bahado-Singh RO 

PROVIDER: S-EPMC6472728 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature