Artificial intelligence for neurodegenerative experimental models
Artificial intelligence for neurodegenerative experimental models
INTRODUCTION: Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials. METHODS: Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research. RESULTS: Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability. DISCUSSION: AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data. Highlights: There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery.
animal models artificial intelligence comparative biology dementia experimental models FAIR in silico in vitro in vivo iPSC machine learning neurodegeneration preclinical reproducibility translation, animal models, comparative biology, FAIR, reproducibility, iPSC, neurodegeneration, in silico, machine learning, artificial intelligence, experimental models, preclinical, in vitro, translation, dementia, in vivo
5970-5987
Marzi, Sarah J.
96bf9876-0b1f-43f5-b48b-bf33aab0fcec
Schilder, Brian M.
f14eef0e-406c-461f-a49d-d1120d1c026f
Nott, Alexi
779134ff-4d0d-41c2-ba04-79807c266175
Frigerio, Carlo Sala
b6b3e045-c91e-4885-8c53-a9f696d77b24
Willaime-Morawek, Sandrine
24a2981f-aa9e-4bf6-ad12-2ccf6b49f1c0
Bucholc, Magda
136e62cb-824e-40da-bfe7-3ab920a9c5f4
Hanger, Diane P.
7f1b5bc4-2410-4239-b6ac-8988f65d2973
James, Charlotte
4529b52e-42cb-4d6a-ad92-ad6d3e75712d
Lewis, Patrick A.
6c99e097-db2e-4a73-8b32-1bf6a3dfd4b3
Lourida, Ilianna
2370e407-6fc9-401e-9226-4ab5f035effb
Noble, Wendy
e53fafef-a7f4-4b9c-982f-65ef7f00c35f
Rodriguez-Algarra, Francisco
3438f58a-ef70-4533-87be-e973a6781188
Sharif, Jalil-Ahmad
3d432213-ba31-4b44-ac7c-6cbf3a6cf836
Tsalenchuk, Maria
bf30fd2b-0a23-4f60-92d0-af35c108ff2e
Winchester, Laura M.
82776935-ffd2-4716-a8ab-c098ec165a2a
Yaman, Ümran
bb64662f-781b-49c5-8aef-033dbb40286a
Yao, Zhi
eaf10b42-d08a-420a-9dfa-d3c372f9e6dd
Ranson, Janice M.
887277d9-29eb-4932-8396-b29ef982ed09
Llewellyn, David J.
4bc78d27-c794-41af-ae08-358a5aeace99
Deep Dementia Phenotyping (DEMON) Network
December 2023
Marzi, Sarah J.
96bf9876-0b1f-43f5-b48b-bf33aab0fcec
Schilder, Brian M.
f14eef0e-406c-461f-a49d-d1120d1c026f
Nott, Alexi
779134ff-4d0d-41c2-ba04-79807c266175
Frigerio, Carlo Sala
b6b3e045-c91e-4885-8c53-a9f696d77b24
Willaime-Morawek, Sandrine
24a2981f-aa9e-4bf6-ad12-2ccf6b49f1c0
Bucholc, Magda
136e62cb-824e-40da-bfe7-3ab920a9c5f4
Hanger, Diane P.
7f1b5bc4-2410-4239-b6ac-8988f65d2973
James, Charlotte
4529b52e-42cb-4d6a-ad92-ad6d3e75712d
Lewis, Patrick A.
6c99e097-db2e-4a73-8b32-1bf6a3dfd4b3
Lourida, Ilianna
2370e407-6fc9-401e-9226-4ab5f035effb
Noble, Wendy
e53fafef-a7f4-4b9c-982f-65ef7f00c35f
Rodriguez-Algarra, Francisco
3438f58a-ef70-4533-87be-e973a6781188
Sharif, Jalil-Ahmad
3d432213-ba31-4b44-ac7c-6cbf3a6cf836
Tsalenchuk, Maria
bf30fd2b-0a23-4f60-92d0-af35c108ff2e
Winchester, Laura M.
82776935-ffd2-4716-a8ab-c098ec165a2a
Yaman, Ümran
bb64662f-781b-49c5-8aef-033dbb40286a
Yao, Zhi
eaf10b42-d08a-420a-9dfa-d3c372f9e6dd
Ranson, Janice M.
887277d9-29eb-4932-8396-b29ef982ed09
Llewellyn, David J.
4bc78d27-c794-41af-ae08-358a5aeace99
Marzi, Sarah J., Schilder, Brian M., Nott, Alexi, Frigerio, Carlo Sala, Willaime-Morawek, Sandrine, Bucholc, Magda, Hanger, Diane P., James, Charlotte, Lewis, Patrick A., Lourida, Ilianna, Noble, Wendy, Rodriguez-Algarra, Francisco, Sharif, Jalil-Ahmad, Tsalenchuk, Maria, Winchester, Laura M., Yaman, Ümran, Yao, Zhi, Ranson, Janice M. and Llewellyn, David J.
,
Deep Dementia Phenotyping (DEMON) Network
(2023)
Artificial intelligence for neurodegenerative experimental models.
Alzheimer's & Dementia, 19 (12), .
(doi:10.1002/alz.13479).
Abstract
INTRODUCTION: Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials. METHODS: Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research. RESULTS: Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability. DISCUSSION: AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data. Highlights: There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery.
Text
Alzheimer s Dementia - 2023 - Marzi - Artificial intelligence for neurodegenerative experimental models
- Version of Record
More information
Accepted/In Press date: 14 August 2023
e-pub ahead of print date: 28 September 2023
Published date: December 2023
Additional Information:
Funding Information:
This manuscript was facilitated by the Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART), through the AI for Precision Dementia Medicine Professional Interest Area (PIA). The views and opinions expressed by authors in this publication represent those of the authors and do not necessarily reflect those of the PIA membership, ISTAART, or the Alzheimer's Association. With thanks to the Deep Dementia Phenotyping (DEMON) Network State of the Science symposium participants (in alphabetical order): Peter Bagshaw, Robin Borchert, Magda Bucholc, James Duce, Charlotte James, David Llewellyn, Donald Lyall, Sarah Marzi, Danielle Newby, Neil Oxtoby, Janice Ranson, Tim Rittman, Nathan Skene, Eugene Tang, Michele Veldsman, Laura Winchester, Zhi Yao. This paper was the product of a DEMON Network State of the Science symposium entitled “Harnessing Data Science and AI in Dementia Research” funded by Alzheimer's Research UK. JMR and DJL are supported by Alzheimer's Research UK and the Alan Turing Institute/Engineering and Physical Sciences Research Council (EP/N510129/1). DJL also receives funding from the Medical Research Council (MR/X005674/1), National Institute for Health Research (NIHR) Applied Research Collaboration South West Peninsula, National Health and Medical Research Council (NHMRC), and National Institute on Aging/National Institutes of Health (RF1AG055654). SJM and AN are funded by the Edmond and Lily Safra Early Career Fellowship Program and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer's Society, and Alzheimer's Research UK. MB is supported by Alzheimer's Research UK, Economic and Social Research Council (ES/W010240/1), EU (SEUPB) INTERREG (ERDF/SEUPB), and HSC R&D (COM/5750/23). PAL acknowledges generous support from the Michael J. Fox Foundation and Parkinson's UK. CJ and LMW are supported by Alzheimer's Research UK.
Publisher Copyright:
© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.
Keywords:
animal models artificial intelligence comparative biology dementia experimental models FAIR in silico in vitro in vivo iPSC machine learning neurodegeneration preclinical reproducibility translation, animal models, comparative biology, FAIR, reproducibility, iPSC, neurodegeneration, in silico, machine learning, artificial intelligence, experimental models, preclinical, in vitro, translation, dementia, in vivo
Identifiers
Local EPrints ID: 482711
URI: http://eprints.soton.ac.uk/id/eprint/482711
ISSN: 1552-5260
PURE UUID: 6e136ea0-db00-4d51-9a54-77578b2f8446
Catalogue record
Date deposited: 11 Oct 2023 16:56
Last modified: 13 Apr 2024 01:42
Export record
Altmetrics
Contributors
Author:
Sarah J. Marzi
Author:
Brian M. Schilder
Author:
Alexi Nott
Author:
Carlo Sala Frigerio
Author:
Magda Bucholc
Author:
Diane P. Hanger
Author:
Charlotte James
Author:
Patrick A. Lewis
Author:
Ilianna Lourida
Author:
Wendy Noble
Author:
Francisco Rodriguez-Algarra
Author:
Jalil-Ahmad Sharif
Author:
Maria Tsalenchuk
Author:
Laura M. Winchester
Author:
Ümran Yaman
Author:
Zhi Yao
Author:
Janice M. Ranson
Author:
David J. Llewellyn
Corporate Author: Deep Dementia Phenotyping (DEMON) Network
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics