Realistic simulation of virtual multi-scale, multi-modal patient trajectories using Bayesian networks and sparse auto-encoders

Translational research of many disease areas requires a longitudinal understanding of disease development and progression across all biologically relevant scales. Several corresponding studies are now available. However, to compile a comprehensive picture of a specific disease, multiple studies need...

Full description

Saved in:

Bibliographic Details
Published in:	Scientific reports Vol. 10; no. 1; p. 10971
Main Authors:	Sood, Meemansa, Sahay, Akrishta, Karki, Reagon, Emon, Mohammad Asif, Vrooman, Henri, Hofmann-Apitius, Martin, Fröhlich, Holger
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 03.07.2020 Nature Publishing Group
Subjects:	692/308/153 692/308/575 Alzheimer Disease - diagnostic imaging Alzheimer Disease - genetics Alzheimer's disease Bayes Theorem Bayesian analysis Brain - diagnostic imaging Brain research Cognitive ability Cohort Studies Computer Simulation Databases, Factual - statistics & numerical data Deep Learning Disease Progression Drug development Humanities and Social Sciences Humans Longitudinal Studies Mathematical models Medical imaging Models, Statistical multidisciplinary Parkinson Disease - diagnosis Parkinson's disease Patients Polymorphism, Single Nucleotide Science Science (multidisciplinary) Simulation Statistical models Translation Translational Research, Biomedical - methods Translational Research, Biomedical - statistics & numerical data User-Computer Interface
ISSN:	2045-2322, 2045-2322
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!