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Artificial Intelligence and Integrated Genotype–Phenotype Identification
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 da...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2018
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6356893/ https://www.ncbi.nlm.nih.gov/pubmed/30597900 http://dx.doi.org/10.3390/genes10010018 |
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author | Frey, Lewis J. |
author_facet | Frey, Lewis J. |
author_sort | Frey, Lewis J. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6356893 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-63568932019-02-04 Artificial Intelligence and Integrated Genotype–Phenotype Identification Frey, Lewis J. Genes (Basel) Perspective 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. MDPI 2018-12-28 /pmc/articles/PMC6356893/ /pubmed/30597900 http://dx.doi.org/10.3390/genes10010018 Text en © 2018 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Perspective Frey, Lewis J. Artificial Intelligence and Integrated Genotype–Phenotype Identification |
title | Artificial Intelligence and Integrated Genotype–Phenotype Identification |
title_full | Artificial Intelligence and Integrated Genotype–Phenotype Identification |
title_fullStr | Artificial Intelligence and Integrated Genotype–Phenotype Identification |
title_full_unstemmed | Artificial Intelligence and Integrated Genotype–Phenotype Identification |
title_short | Artificial Intelligence and Integrated Genotype–Phenotype Identification |
title_sort | artificial intelligence and integrated genotype–phenotype identification |
topic | Perspective |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6356893/ https://www.ncbi.nlm.nih.gov/pubmed/30597900 http://dx.doi.org/10.3390/genes10010018 |
work_keys_str_mv | AT freylewisj artificialintelligenceandintegratedgenotypephenotypeidentification |