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Precision cardiovascular medicine: artificial intelligence and epigenetics for the pathogenesis and prediction of coarctation in neonates.

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TLDR
The use of AI and epigenomics to achieve important objectives of precision cardiovascular medicine is reported, providing evidence of a significant epigenetic etiology in isolated CoA development.
Abstract
Background: Advances in omics and computational Artificial Intelligence (AI) have been said to be key to meeting the objectives of precision cardiovascular medicine. The focus of precision medicine includes a better assessment of disease risk and understanding of disease mechanisms. Our objective was to determine whether significant epigenetic changes occur in isolated, non-syndromic CoA. Further, we evaluated the AI analysis of DNA methylation for the prediction of CoA.Methods: Genome-wide DNA methylation analysis of newborn blood DNA was performed in 24 isolated, non-syndromic CoA cases and 16 controls using the Illumina HumanMethylation450 BeadChip arrays. Cytosine nucleotide (CpG) methylation changes in CoA in each of 450,000 CpG loci were determined. Ingenuity pathway analysis (IPA) was performed to identify molecular and disease pathways that were epigenetically dysregulated. Using methylation data, six artificial intelligence (AI) platforms including deep learning (DL) was used for CoA detection.Results: We identified significant (FDR p-value ≤ .05) methylation changes in 65 different CpG sites located in 75 genes in CoA subjects. DL achieved an AUC (95% CI) = 0.97 (0.80-1) with 95% sensitivity and 98% specificity. Gene ontology (GO) analysis yielded epigenetic alterations in important cardiovascular developmental genes and biological processes: abnormal morphology of cardiovascular system, left ventricular dysfunction, heart conduction disorder, thrombus formation, and coronary artery disease.Conclusion: In an exploratory study we report the use of AI and epigenomics to achieve important objectives of precision cardiovascular medicine. Accurate prediction of CoA was achieved using a newborn blood spot. Further, we provided evidence of a significant epigenetic etiology in isolated CoA development.

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The Role of Epigenetics in Congenital Heart Disease

TL;DR: The role of epigenetic regulation in cardiac development/cardiogenesis, using tissue and animal models, has been well reviewed in this article, with an aim to provide scientists and clinicians an overview of the abnormal cardiogenic pathways affected by epigenetic mechanisms.
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The role of DNA methylation in syndromic and non-syndromic congenital heart disease

TL;DR: The role of epigenetics in various diseases, including CHD, has attracted increased attention as discussed by the authors, but the contributions of DNA methylation, one of the most important epigenetic modifications, to CHD have not been illuminated.
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Diagnostic Accuracy of Machine Learning Models to Identify Congenital Heart Disease: A Meta-Analysis.

TL;DR: A comprehensive literature search in PubMed, CINAHL, Wiley Cochrane Library, and Web of Science databases was performed to estimate the diagnostic accuracy of machine learning (ML) models for detecting CHD as mentioned in this paper.
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Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment.

TL;DR: A survey of the role of AI and microfluidics in biotechnology is presented and recent studies that perform flow cytometry cell classification, cell isolation, and a combination of them by gaining from both AI methods and micro fluidic techniques are covered.
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Probabilistic domain-knowledge modeling of disorder pathogenesis for dynamics forecasting of acute onset.

TL;DR: A novel pathogenesis probabilistic graphical model (PPGM) to quantify the dynamics underpinning patient-specific data and pathogenetic domain knowledge and perform medical inferences and its interpretability for physicians is proposed.
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Deep Learning in Bioinformatics

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Artificial Intelligence in Precision Cardiovascular Medicine

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