Bettencourt, Conceicao;
Skene, Nathan;
Bandres-Ciga, Sara;
Anderson, Emma;
Winchester, Laura M;
Foote, Isabelle F;
Schwartzentruber, Jeremy;
... Llewellyn, David J; + view all
(2023)
Artificial intelligence for dementia genetics and omics.
Alzheimer’s & Dementia: The Journal of the Alzheimer's Association
pp. 1-17.
10.1002/alz.13427.
(In press).
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Abstract
Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine.
| Type: | Article |
|---|---|
| Title: | Artificial intelligence for dementia genetics and omics |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/alz.13427 |
| Publisher version: | https://doi.org/10.1002/alz.13427 |
| Language: | English |
| Additional information: | Copyright © 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association. This is an open access article under the terms of the Creative Commons Attribution License, https://creativecommons.org/licenses/by-nc-nd/4.0/, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Artificial intelligence, biomarkers, pathology, causality, dementia, disease pathways, etiology, genetics, machine learning, omics, risk factors |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neurodegenerative Diseases |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10176223 |
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