Multiomics, artificial intelligence, and precision medicine in perinatology.
Journal
Pediatric research
ISSN: 1530-0447
Titre abrégé: Pediatr Res
Pays: United States
ID NLM: 0100714
Informations de publication
Date de publication:
01 2023
01 2023
Historique:
received:
22
03
2022
accepted:
30
05
2022
revised:
12
05
2022
pubmed:
9
7
2022
medline:
25
2
2023
entrez:
8
7
2022
Statut:
ppublish
Résumé
Technological advances in omics evaluation, bioinformatics, and artificial intelligence have made us rethink ways to improve patient outcomes. Collective quantification and characterization of biological data including genomics, epigenomics, metabolomics, and proteomics is now feasible at low cost with rapid turnover. Significant advances in the integration methods of these multiomics data sets by machine learning promise us a holistic view of disease pathogenesis and yield biomarkers for disease diagnosis and prognosis. Using machine learning tools and algorithms, it is possible to integrate multiomics data with clinical information to develop predictive models that identify risk before the condition is clinically apparent, thus facilitating early interventions to improve the health trajectories of the patients. In this review, we intend to update the readers on the recent developments related to the use of artificial intelligence in integrating multiomic and clinical data sets in the field of perinatology, focusing on neonatal intensive care and the opportunities for precision medicine. We intend to briefly discuss the potential negative societal and ethical consequences of using artificial intelligence in healthcare. We are poised for a new era in medicine where computational analysis of biological and clinical data sets will make precision medicine a reality. IMPACT: Biotechnological advances have made multiomic evaluations feasible and integration of multiomics data may provide a holistic view of disease pathophysiology. Artificial Intelligence and machine learning tools are being increasingly used in healthcare for diagnosis, prognostication, and outcome predictions. Leveraging artificial intelligence and machine learning tools for integration of multiomics and clinical data will pave the way for precision medicine in perinatology.
Identifiants
pubmed: 35804156
doi: 10.1038/s41390-022-02181-x
pii: 10.1038/s41390-022-02181-x
pmc: PMC9825681
mid: NIHMS1818151
doi:
Types de publication
Journal Article
Review
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
308-315Subventions
Organisme : NICHD NIH HHS
ID : R03 HD098482
Pays : United States
Organisme : NICHD NIH HHS
ID : R21 HD091718
Pays : United States
Informations de copyright
© 2022. The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc.
Références
AAPS J. 2021 May 18;23(4):74
pubmed: 34008139
Sci Rep. 2018 Sep 13;8(1):13743
pubmed: 30213963
Science. 1966 Jul 1;153(3731):34-7
pubmed: 17730601
Soc Sci Med. 2020 Sep;260:113172
pubmed: 32702587
JAMA. 2020 Aug 25;324(8):735-736
pubmed: 32766768
Semin Fetal Neonatal Med. 2018 Dec;23(6):370-373
pubmed: 30100524
Genes (Basel). 2019 Mar 20;10(3):
pubmed: 30897838
Nat Med. 2018 Sep;24(9):1337-1341
pubmed: 30104767
J Med Syst. 2018 Oct 8;42(11):226
pubmed: 30298337
J Biomed Inform. 2022 Apr;128:104031
pubmed: 35183765
Genome Biol. 2017 May 5;18(1):83
pubmed: 28476144
Front Med (Lausanne). 2022 Jan 11;8:821756
pubmed: 35087854
BMC Bioinformatics. 2020 Jan 9;21(1):9
pubmed: 31918677
Prog Retin Eye Res. 2022 May;88:101018
pubmed: 34763060
Fetal Diagn Ther. 2020;47(5):363-372
pubmed: 31910421
Gut. 2019 Dec;68(12):2161-2169
pubmed: 30858305
Arch Dis Child Fetal Neonatal Ed. 2022 May;107(3):336-339
pubmed: 34257102
Front Pediatr. 2021 Sep 03;9:724280
pubmed: 34540772
J Pediatr Surg. 2021 Oct;56(10):1703-1710
pubmed: 33342603
Gut. 2019 Oct;68(10):1813-1819
pubmed: 30814121
JAMA Netw Open. 2020 Dec 1;3(12):e2029655
pubmed: 33337494
Pediatr Res. 2020 Aug;88(Suppl 1):16-20
pubmed: 32855507
Trends Mol Med. 2021 Aug;27(8):762-776
pubmed: 33573911
Bioinformatics. 2019 Jan 1;35(1):95-103
pubmed: 30561547
Cancers (Basel). 2019 Aug 23;11(9):
pubmed: 31450799
Lancet Diabetes Endocrinol. 2018 May;6(5):416-426
pubmed: 29433995
Kidney Dis (Basel). 2019 Feb;5(1):11-17
pubmed: 30815459
iScience. 2022 Jan 22;25(2):103798
pubmed: 35169688
Curr Opin Endocrinol Diabetes Obes. 2021 Dec 1;28(6):553-557
pubmed: 34709211
Pediatrics. 2021 Dec 1;148(6):
pubmed: 34814160
Comput Struct Biotechnol J. 2021 Jun 22;19:3735-3746
pubmed: 34285775
Transl Vis Sci Technol. 2020 Feb 10;9(2):5
pubmed: 32704411
Metabolites. 2020 Apr 08;10(4):
pubmed: 32276350
PLoS One. 2014 Feb 28;9(2):e89860
pubmed: 24587080
Expert Rev Proteomics. 2021 Apr;18(4):247-259
pubmed: 33896313
Womens Health (Lond). 2021 Jan-Dec;17:17455065211046132
pubmed: 34519596
Dig Dis Sci. 2020 Mar;65(3):789-796
pubmed: 32008132
Inf Fusion. 2019 Oct;50:71-91
pubmed: 30467459
Microbiome. 2017 Mar 9;5(1):31
pubmed: 28274256
Cell. 2015 Nov 19;163(5):1079-1094
pubmed: 26590418
Retina. 2022 Jan 1;42(1):195-203
pubmed: 34387234
Ophthalmology. 2019 Apr;126(4):552-564
pubmed: 30553900
Pediatr Res. 2022 Feb;91(3):590-597
pubmed: 34021272
J Neonatal Perinatal Med. 2020;13(3):373-380
pubmed: 31985475
Am J Clin Nutr. 2019 Jul 1;110(1):63-75
pubmed: 31095300
Mol Syst Biol. 2018 Jun 20;14(6):e8124
pubmed: 29925568
Sci Transl Med. 2021 May 5;13(592):
pubmed: 33952678
Front Artif Intell. 2022 May 11;5:889981
pubmed: 35647529
Proc ACM Conf Health Inference Learn (2020). 2020 Apr;2020:99-109
pubmed: 34318306
Z Med Phys. 2019 May;29(2):102-127
pubmed: 30553609