Transcriptomics and machine learning predict diagnosis and severity of growth hormone deficiency.
Journal:
JCI insight
PMID:
29618660
Abstract
BACKGROUND: The effect of gene expression data on diagnosis remains limited. Here, we show how diagnosis and classification of growth hormone deficiency (GHD) can be achieved from a single blood sample using a combination of transcriptomics and random forest analysis.
Authors
Keywords
Adolescent
Biomarkers
Case-Control Studies
Child
Child, Preschool
Datasets as Topic
Female
Gene Expression Profiling
Gene Regulatory Networks
Growth Disorders
Healthy Volunteers
Human Growth Hormone
Humans
Machine Learning
Male
Polymorphism, Single Nucleotide
Predictive Value of Tests
Principal Component Analysis
ROC Curve
Severity of Illness Index
Transcriptome