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

Ilan Shomorony,Elizabeth T. Cirulli,Lei Huang,Lori A. Napier,Robyn Heister,Michael Hicks,Isaac V. Cohen,Hung-Chun Yu,Christine Leon Swisher,Natalie M. Schenker-Ahmed,Weizhong Li,Karen E. Nelson,Pamila Brar,Andrew M. Kahn,Tim D. Spector,C. Thomas Caskey,J. Craig Venter,David S. Karow,Ewen F. Kirkness,Naisha Shah

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

2020

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.

Keywords:

Disease
Modalities
modern medicine
Machine learning
Biology
Unsupervised learning
Artificial intelligence
Bioinformatics
Biomarker (medicine)
Health risk assessment
Body mass index
Human genetics
Metabolome
Microbiome

Correction
Source
Cite
Save
Machine Reading By IdeaReader

References

Citations