AttOmics: attention-based architecture for diagnosis and prognosis from omics data - Algorithmique, Recherche Opérationnelle, Bioinformatique et Apprentissage Statistique Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

AttOmics: attention-based architecture for diagnosis and prognosis from omics data

Milad Rafiee Vahid
Franck Augé

Résumé

Motivation The increasing availability of high-throughput omics data allows for considering a new medicine centered on individual patients. Precision medicine relies on exploiting these high-throughput data with machine-learning models, especially the ones based on deep-learning approaches, to improve diagnosis. Due to the high-dimensional small-sample nature of omics data, current deep-learning models end up with many parameters and have to be fitted with a limited training set. Furthermore, interactions between molecular entities inside an omics profile are not patient specific but are the same for all patients. Results In this article, we propose AttOmics, a new deep-learning architecture based on the self-attention mechanism. First, we decompose each omics profile into a set of groups, where each group contains related features. Then, by applying the self-attention mechanism to the set of groups, we can capture the different interactions specific to a patient. The results of different experiments carried out in this article show that our model can accurately predict the phenotype of a patient with fewer parameters than deep neural networks. Visualizing the attention maps can provide new insights into the essential groups for a particular phenotype. Availability and implementation The code and data are available at https://forge.ibisc.univ-evry.fr/abeaude/AttOmics. TCGA data can be downloaded from the Genomic Data Commons Data Portal.
Fichier principal
Vignette du fichier
main.pdf (978.06 Ko) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04431940 , version 1 (01-02-2024)

Identifiants

Citer

Aurélien Beaude, Milad Rafiee Vahid, Franck Augé, Farida Zehraoui, Blaise Hanczar. AttOmics: attention-based architecture for diagnosis and prognosis from omics data. Intelligent Systems for Molecular Biology, Jun 2023, lyon, France. pp.i94-i102, ⟨10.1093/bioinformatics/btad232⟩. ⟨hal-04431940⟩
19 Consultations
1 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More