Mechanism-aware and multimodal AI: beyond model-agnostic interpretation.

imaging data mechanism-aware AI metabolic modelling multi-omics multimodal integration

Journal

Trends in cell biology
ISSN: 1879-3088
Titre abrégé: Trends Cell Biol
Pays: England
ID NLM: 9200566

Informations de publication

Date de publication:
11 Dec 2023
Historique:
received: 24 07 2023
revised: 03 11 2023
accepted: 07 11 2023
medline: 13 12 2023
pubmed: 13 12 2023
entrez: 13 12 2023
Statut: aheadofprint

Résumé

Artificial intelligence (AI) is widely used for exploiting multimodal biomedical data, with increasingly accurate predictions and model-agnostic interpretations, which are however also agnostic to biological mechanisms. Combining metabolic modelling, 'omics, and imaging data via multimodal AI can generate predictions that can be interpreted mechanistically and transparently, therefore with significantly higher therapeutic potential.

Identifiants

pubmed: 38087709
pii: S0962-8924(23)00235-0
doi: 10.1016/j.tcb.2023.11.002
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of interests None declared by authors.

Auteurs

Annalisa Occhipinti (A)

School of Computing, Engineering and Digital Technologies, Teesside University, Middlesborough, UK; Centre for Digital Innovation, Teesside University, Middlesborough, UK; National Horizons Centre, Teesside University, Darlington, UK.

Suraj Verma (S)

School of Computing, Engineering and Digital Technologies, Teesside University, Middlesborough, UK.

Le Minh Thao Doan (LMT)

School of Computing, Engineering and Digital Technologies, Teesside University, Middlesborough, UK.

Claudio Angione (C)

School of Computing, Engineering and Digital Technologies, Teesside University, Middlesborough, UK; Centre for Digital Innovation, Teesside University, Middlesborough, UK; National Horizons Centre, Teesside University, Darlington, UK. Electronic address: c.angione@tees.ac.uk.

Classifications MeSH