Artificial Intelligence in Molecular Medicine

Machine-learning methods for analyzing genomic, transcriptomic, epigenomic, proteomic, and metabolomic data sets have yielded clinically directive information, mostly for rare genetic diseases.

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Bibliographic Details
Published in:	The New England journal of medicine Vol. 388; no. 26; pp. 2456 - 2465
Main Authors:	Gomes, Bruna, Ashley, Euan A.
Format:	Journal Article
Language:	English
Published:	United States Massachusetts Medical Society 29-06-2023
Series:	AI in Medicine
Subjects:	Accuracy and Education and Education General Artificial Intelligence Clinical Medicine Clinical Medicine General Ethical and Legal Issues Genes Genetic disorders Genetics Genetics General Genomes Genomics Health IT Health Policy Humans Ions Machine learning Mass spectrometry Medical Ethics Medical Practice Metabolites Metabolomics Molecular Medicine Neural networks Proteins Proteomics Quality of Care Rare diseases Research Ethics Scientific imaging Training Transcriptomics
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Description
Summary:	Machine-learning methods for analyzing genomic, transcriptomic, epigenomic, proteomic, and metabolomic data sets have yielded clinically directive information, mostly for rare genetic diseases.
Bibliography:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-3 content type line 23 ObjectType-Review-1
ISSN:	0028-4793 1533-4406
DOI:	10.1056/NEJMra2204787