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...

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Veröffentlicht in:	Scientific reports Jg. 10; H. 1; S. 10971
Hauptverfasser:	Sood, Meemansa, Sahay, Akrishta, Karki, Reagon, Emon, Mohammad Asif, Vrooman, Henri, Hofmann-Apitius, Martin, Fröhlich, Holger
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 03.07.2020 Nature Publishing Group
Schlagworte:	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
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Abstract	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 to be analyzed and compared. A large number of clinical studies is nowadays conducted in the context of drug development in pharmaceutical research. However, legal and ethical constraints typically do not allow for sharing sensitive patient data. In consequence there exist data “silos”, which slow down the overall scientific progress in translational research. In this paper, we suggest the idea of a virtual cohort (VC) to address this limitation. Our key idea is to describe a longitudinal patient cohort with the help of a generative statistical model, namely a modular Bayesian Network, in which individual modules are represented as sparse autoencoder networks. We show that with the help of such a model we can simulate subjects that are highly similar to real ones. Our approach allows for incorporating arbitrary multi-scale, multi-modal data without making specific distribution assumptions. Moreover, we demonstrate the possibility to simulate interventions (e.g. via a treatment) in the VC. Overall, our proposed approach opens the possibility to build sufficiently realistic VCs for multiple disease areas in the future.
AbstractList	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 to be analyzed and compared. A large number of clinical studies is nowadays conducted in the context of drug development in pharmaceutical research. However, legal and ethical constraints typically do not allow for sharing sensitive patient data. In consequence there exist data "silos", which slow down the overall scientific progress in translational research. In this paper, we suggest the idea of a virtual cohort (VC) to address this limitation. Our key idea is to describe a longitudinal patient cohort with the help of a generative statistical model, namely a modular Bayesian Network, in which individual modules are represented as sparse autoencoder networks. We show that with the help of such a model we can simulate subjects that are highly similar to real ones. Our approach allows for incorporating arbitrary multi-scale, multi-modal data without making specific distribution assumptions. Moreover, we demonstrate the possibility to simulate interventions (e.g. via a treatment) in the VC. Overall, our proposed approach opens the possibility to build sufficiently realistic VCs for multiple disease areas in the future. 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 to be analyzed and compared. A large number of clinical studies is nowadays conducted in the context of drug development in pharmaceutical research. However, legal and ethical constraints typically do not allow for sharing sensitive patient data. In consequence there exist data "silos", which slow down the overall scientific progress in translational research. In this paper, we suggest the idea of a virtual cohort (VC) to address this limitation. Our key idea is to describe a longitudinal patient cohort with the help of a generative statistical model, namely a modular Bayesian Network, in which individual modules are represented as sparse autoencoder networks. We show that with the help of such a model we can simulate subjects that are highly similar to real ones. Our approach allows for incorporating arbitrary multi-scale, multi-modal data without making specific distribution assumptions. Moreover, we demonstrate the possibility to simulate interventions (e.g. via a treatment) in the VC. Overall, our proposed approach opens the possibility to build sufficiently realistic VCs for multiple disease areas in the future.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 to be analyzed and compared. A large number of clinical studies is nowadays conducted in the context of drug development in pharmaceutical research. However, legal and ethical constraints typically do not allow for sharing sensitive patient data. In consequence there exist data "silos", which slow down the overall scientific progress in translational research. In this paper, we suggest the idea of a virtual cohort (VC) to address this limitation. Our key idea is to describe a longitudinal patient cohort with the help of a generative statistical model, namely a modular Bayesian Network, in which individual modules are represented as sparse autoencoder networks. We show that with the help of such a model we can simulate subjects that are highly similar to real ones. Our approach allows for incorporating arbitrary multi-scale, multi-modal data without making specific distribution assumptions. Moreover, we demonstrate the possibility to simulate interventions (e.g. via a treatment) in the VC. Overall, our proposed approach opens the possibility to build sufficiently realistic VCs for multiple disease areas in the future.
ArticleNumber	10971
Author	Sahay, Akrishta Vrooman, Henri Karki, Reagon Emon, Mohammad Asif Sood, Meemansa Hofmann-Apitius, Martin Fröhlich, Holger
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CitedBy_id	crossref_primary_10_1038_s41746_022_00666_x crossref_primary_10_1371_journal_pone_0280609 crossref_primary_10_1038_s41531_025_00983_4 crossref_primary_10_1038_s41598_024_62102_2 crossref_primary_10_1016_j_jbi_2021_103837 crossref_primary_10_1016_j_ecoinf_2022_101624
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SubjectTerms	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
Title	Realistic simulation of virtual multi-scale, multi-modal patient trajectories using Bayesian networks and sparse auto-encoders
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