Pubblicazioni

Autori:

Bettencourt, Conceicao; Skene, Nathan; Bandres-Ciga, Sara; Anderson, Emma; Winchester, Laura M; Foote, Isabelle F; Schwartzentruber, Jeremy; Botia, Juan A; Nalls, Mike; Singleton, Andrew; Schilder, Brian M; Humphrey, Jack; Marzi, Sarah J; Toomey, Christina E; Kleifat, Ahmad Al; Harshfield, Eric L; Garfield, Victoria; Sandor, Cynthia; Keat, Samuel; Tamburin, Stefano; Frigerio, Carlo Sala; Lourida, Ilianna; Ranson, Janice M; Llewellyn, David J

Titolo:

Artificial intelligence for dementia genetics and omics

Anno:

2023

Tipologia prodotto:

Articolo in Rivista

Tipologia ANVUR:

Articolo su rivista

Lingua:

Inglese

Formato:

A Stampa

Referee:

Sì

Nome rivista:

ALZHEIMER'S & DEMENTIA (PRINT)

ISSN Rivista:

1552-5260

N° Volume:

19

Numero o Fascicolo:

12

Intervallo pagine:

5905-5921

Parole chiave:

artificial intelligence, biomarkers; pathology, causality, dementia, disease pathways, etiology, genetics, machine learning, omics, risk factors

Breve descrizione dei contenuti:

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. HIGHLIGHTS: We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research.

Pagina Web:

https://doi.org/10.1002/alz.13427

Id prodotto:

134992

Handle IRIS:

11562/1102446

ultima modifica:

4 gennaio 2024

Citazione bibliografica:

Bettencourt, Conceicao; Skene, Nathan; Bandres-Ciga, Sara; Anderson, Emma; Winchester, Laura M; Foote, Isabelle F; Schwartzentruber, Jeremy; Botia, Juan A; Nalls, Mike; Singleton, Andrew; Schilder, Brian M; Humphrey, Jack; Marzi, Sarah J; Toomey, Christina E; Kleifat, Ahmad Al; Harshfield, Eric L; Garfield, Victoria; Sandor, Cynthia; Keat, Samuel; Tamburin, Stefano; Frigerio, Carlo Sala; Lourida, Ilianna; Ranson, Janice M; Llewellyn, David J, Artificial intelligence for dementia genetics and omics «ALZHEIMER'S & DEMENTIA (PRINT)» , vol. 19 , n. 12 , 2023 , pp. 5905-5921

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