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Int J Mol Sci. 2019 Apr 27;20(9). pii: E2075. doi: 10.3390/ijms20092075.

Deep Learning/Artificial Intelligence and Blood-Based DNA Epigenomic Prediction of Cerebral Palsy.

Author information

1
Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI 48073, USA. Ray.Bahado-Singh@beaumont.org.
2
Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI 48073, USA. Sangeetha.Vishweswaraiah@beaumont.org.
3
Department of Mathematics & Computer Science, Albion College, Albion, MI 49224, USA. baydas@albion.edu.
4
Dept. of Genetics, Cell Biology & Anatomy, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68182, USA. nitish.mishra@unmc.edu.
5
Dept. of Genetics, Cell Biology & Anatomy, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68182, USA. babu.guda@unmc.edu.
6
Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI 48073, USA. Uppala.radhakrishna@beaumont.edu.

Abstract

The etiology of cerebral palsy (CP) is complex and remains inadequately understood. Early detection of CP is an important clinical objective as this improves long term outcomes. We performed genome-wide DNA methylation analysis to identify epigenomic predictors of CP in newborns and to investigate disease pathogenesis. Methylation analysis of newborn blood DNA using an Illumina HumanMethylation450K array was performed in 23 CP cases and 21 unaffected controls. There were 230 significantly differentially-methylated CpG loci in 258 genes. Each locus had at least 2.0-fold change in methylation in CP versus controls with a FDR p-value ≤ 0.05. Methylation level for each CpG locus had an area under the receiver operating curve (AUC) ≥ 0.75 for CP detection. Using Artificial Intelligence (AI) platforms/Machine Learning (ML) analysis, CpG methylation levels in a combination of 230 significantly differentially-methylated CpG loci in 258 genes had a 95% sensitivity and 94.4% specificity for newborn prediction of CP. Using pathway analysis, multiple canonical pathways plausibly linked to neuronal function were over-represented. Altered biological processes and functions included: neuromotor damage, malformation of major brain structures, brain growth, neuroprotection, neuronal development and de-differentiation, and cranial sensory neuron development. In conclusion, blood leucocyte epigenetic changes analyzed using AI/ML techniques appeared to accurately predict CP and provided plausible mechanistic information on CP pathogenesis.

KEYWORDS:

DNA methylation; cerebral palsy; epigenetics; neurodegenerative disorders; newborns

PMID:
31035542
PMCID:
PMC6539236
DOI:
10.3390/ijms20092075
[Indexed for MEDLINE]
Free PMC Article

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