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Artificial Intelligence and Integrated Genotype⁻Phenotype Identification

Overview
Journal Genes (Basel)
Publisher MDPI
Date 2019 Jan 2
PMID 30597900
Citations 10
Authors
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Abstract

The integration of phenotypes and genotypes is at an unprecedented level and offers new opportunities to establish deep phenotypes. There are a number of challenges to overcome, specifically, accelerated growth of data, data silos, incompleteness, inaccuracies, and heterogeneity within and across data sources. This perspective report discusses artificial intelligence (AI) approaches that hold promise in addressing these challenges by automating computable phenotypes and integrating them with genotypes. Collaborations between biomedical and AI researchers will be highlighted in order to describe initial successes with an eye toward the future.

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References
1.
Collins F, Varmus H . A new initiative on precision medicine. N Engl J Med. 2015; 372(9):793-5. PMC: 5101938. DOI: 10.1056/NEJMp1500523. View

2.
Frey L, Lenert L, Lopez-Campos G . EHR Big Data Deep Phenotyping. Contribution of the IMIA Genomic Medicine Working Group. Yearb Med Inform. 2014; 9:206-11. PMC: 4287080. DOI: 10.15265/IY-2014-0006. View

3.
Alipanahi B, Delong A, Weirauch M, Frey B . Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol. 2015; 33(8):831-8. DOI: 10.1038/nbt.3300. View

4.
Bejnordi B, Veta M, van Diest P, van Ginneken B, Karssemeijer N, Litjens G . Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA. 2017; 318(22):2199-2210. PMC: 5820737. DOI: 10.1001/jama.2017.14585. View

5.
Silver D, Huang A, Maddison C, Guez A, Sifre L, Van Den Driessche G . Mastering the game of Go with deep neural networks and tree search. Nature. 2016; 529(7587):484-9. DOI: 10.1038/nature16961. View