An unsupervised learning approach to identify novel signatures of health and disease from multimodal data

An unsupervised learning approach to identify novel signatures of health and disease from multimodal data

Abstract Background Modern medicine is rapidly moving towards a data-driven paradigm based on comprehensive multimodal health assessments. Integrated analysis of data from different modalities has the potential of uncovering novel biomarkers and disease signatures. Methods We collected 1385 data fea...

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Hlavní autoři: Ilan Shomorony, Elizabeth T. Cirulli, Lei Huang, Lori Napier, Robyn Heister, Michael Hicks, Isaac Cohen, Hung‐Chun Yu, Christine Leon Swisher, Natalie M. Schenker-Ahmed, Weizhong Li, William Nelson, Pamila Brar, Andrew M. Kahn, Timothy D. Spector, C. Thomas Caskey, J. Craig Venter, David S. Karow, Ewen F. Kirkness, Naisha Shah
Médium: Artigo
Jazyk:angličtina
Vydáno: 2020
On-line přístup:https://doi.org/10.1186/s13073-019-0705-z
https://genomemedicine.biomedcentral.com/track/pdf/10.1186/s13073-019-0705-z
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https://doi.org/10.1186/s13073-019-0705-z
https://genomemedicine.biomedcentral.com/track/pdf/10.1186/s13073-019-0705-z

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