Research Topic

Computational approaches for ageing and age-related diseases

About this Research Topic

Ageing is a complex phenomenon that remains poorly understood and raises great challenges for science, medicine, and society. Age-related diseases, such as neurodegenerative diseases, remain largely uncured, with attrition rates in clinical trials reaching unprecedented levels. There is no consensus on prevention measures due to limited understanding of the complex interplay between the multiple manifestations of ageing across scales, systems and organs. The spectrum of possible trajectories of healthy and pathological ageing is extraordinarily entangled, multifactorial, and heterogeneous.

Describing, modelling, and predicting the progression of slowly evolving biological processes requires the development of specific computational and data-driven methods at the cross-roads of biostatistics, machine learning, mathematical modeling, knowledge modeling and numerical simulation.

CompAge 2020 aims to be a first-of-its-kind forum to communicate recent methodological advances in this field, and foster interactions among researchers from academia, pharmaceutical and technology industries, clinical research, and public health sectors. This Research Topic welcomes contributions presented in the context of the CompAge 2020 workshop and encourages other contributions that match with the scope of the topic, as outlined below.

Topics of interest include, but are not limited to:

- Methods to describe, classify and represent the heterogeneity of individual trajectories of ageing from multimodal and longitudinal data sets, with the aim, for instance, to understand how genetic, lifestyle or environmental factors affect ageing;
- Dynamical models of disease progression integrating various clinical or preclinical data across scales and organs, including omics, cellular and medical imaging, physiological, cognitive, behavioral, and clinical assessments in health or disease, in humans or animal models;
- Mechanistic models of disease progression, such as pathogen spreading models in neurodegenerative diseases;
- Development of data-driven tools for precision medicine including personalised prediction of risks, prediction of future adverse events, and recommendations of personalised prevention or therapeutic strategies;
- Development of novel strategies for the identification, screening and stratification of the at-risk population, including the use of digital devices or sensor data;
- Development of new clinical trial design to assess the efficacy of disease-modifying agents in progressive diseases, including methods for simulation of treatment effects on ageing or disease progression;
- Methods for the analysis of epidemiological or real-world data sets with the aim to identify risk factors, or assess the long-term efficacy of public health policies.

CompAge 2020 will be the final workshop of the EuroPOND* project, funded within the Horizon 2020 program of the European Union, and the follow-up of a disease progression modelling workshop funded by several Innovative Medicines Initiative (IMI) projects.

We encourage contributions from early career researchers from academia and industry.


Keywords: ageing, neurodegenereative diseases, data-driven methods, machine learning, mathematical modeling, real-world data


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Ageing is a complex phenomenon that remains poorly understood and raises great challenges for science, medicine, and society. Age-related diseases, such as neurodegenerative diseases, remain largely uncured, with attrition rates in clinical trials reaching unprecedented levels. There is no consensus on prevention measures due to limited understanding of the complex interplay between the multiple manifestations of ageing across scales, systems and organs. The spectrum of possible trajectories of healthy and pathological ageing is extraordinarily entangled, multifactorial, and heterogeneous.

Describing, modelling, and predicting the progression of slowly evolving biological processes requires the development of specific computational and data-driven methods at the cross-roads of biostatistics, machine learning, mathematical modeling, knowledge modeling and numerical simulation.

CompAge 2020 aims to be a first-of-its-kind forum to communicate recent methodological advances in this field, and foster interactions among researchers from academia, pharmaceutical and technology industries, clinical research, and public health sectors. This Research Topic welcomes contributions presented in the context of the CompAge 2020 workshop and encourages other contributions that match with the scope of the topic, as outlined below.

Topics of interest include, but are not limited to:

- Methods to describe, classify and represent the heterogeneity of individual trajectories of ageing from multimodal and longitudinal data sets, with the aim, for instance, to understand how genetic, lifestyle or environmental factors affect ageing;
- Dynamical models of disease progression integrating various clinical or preclinical data across scales and organs, including omics, cellular and medical imaging, physiological, cognitive, behavioral, and clinical assessments in health or disease, in humans or animal models;
- Mechanistic models of disease progression, such as pathogen spreading models in neurodegenerative diseases;
- Development of data-driven tools for precision medicine including personalised prediction of risks, prediction of future adverse events, and recommendations of personalised prevention or therapeutic strategies;
- Development of novel strategies for the identification, screening and stratification of the at-risk population, including the use of digital devices or sensor data;
- Development of new clinical trial design to assess the efficacy of disease-modifying agents in progressive diseases, including methods for simulation of treatment effects on ageing or disease progression;
- Methods for the analysis of epidemiological or real-world data sets with the aim to identify risk factors, or assess the long-term efficacy of public health policies.

CompAge 2020 will be the final workshop of the EuroPOND* project, funded within the Horizon 2020 program of the European Union, and the follow-up of a disease progression modelling workshop funded by several Innovative Medicines Initiative (IMI) projects.

We encourage contributions from early career researchers from academia and industry.


Keywords: ageing, neurodegenereative diseases, data-driven methods, machine learning, mathematical modeling, real-world data


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

01 October 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

01 October 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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