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Multiomics, artificial intelligence, and precision medicine in perinatology

Abstract

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.

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Fig. 1: Three eras of medicine.
Fig. 2: Number of publications per year on search keywords from 2010 to 2021.
Fig. 3: Schema for integration of omics data and approaches to disease (reproduced from Hasan et al., open access data).58
Fig. 4: Illustration of the timing of integration for multiomic data matrices (reproduced from Cai et al.10)
Fig. 5: Diagnosis of retinopathy of prematurity by machine learning.

Data availability

This is a review and all data discussed are included in the review.

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Funding

This study was funded by the following extramural sources: NIH R03HD098482, R21HD091718, CA125123; NCI P30CA125123; NIH/NCI R01CA220297, R01CA216426; NIEHS P30 ES030285, P42 ES027725, NIMHD P50MD015496; CPRIT RP170005, R35GM138353, Burroughs Wellcome Fund (1019816), and the March of Dimes.

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M.P. and J.N. wrote the initial draft of the manuscript. N.A. made revisions and provided critical intellectual input for the review.

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Correspondence to Mohan Pammi.

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Pammi, M., Aghaeepour, N. & Neu, J. Multiomics, artificial intelligence, and precision medicine in perinatology. Pediatr Res (2022). https://doi.org/10.1038/s41390-022-02181-x

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