‘Overcoming the Bottleneck’: Knowledge Architectures for Genomic Data Interpretation in Oncology - Aix-Marseille Université Accéder directement au contenu
Ouvrages Année : 2020

‘Overcoming the Bottleneck’: Knowledge Architectures for Genomic Data Interpretation in Oncology

Résumé

In recent years, oncology transitioned from its traditional, organ-based approach to 'precision oncology' centered on molecular alterations. As a result, it has become to a significant extent a 'data-centric' domain. Its practices increasingly rely on a sophisticated techno-scientific infrastructure that generates massive amounts of data in need of consistent, appropriate interpretations. Attempts to overcome the interpretation bottleneck have led to the establishment of a complex landscape of interrelated resources that, while displaying distinct characteristics and design choices, also entertain horizontal and vertical relations. Although there is no denying that the data-centric nature of contemporary oncology raises a number of key issues related to the production and circulation of data, we suggest that the focus on data use and re-use should be complemented by a focus on interpretation. Oncology practitioners refer to data interpretation resources as 'knowledgebases', an actor's category designed to differentiate them from generic, multipurpose databases. Their major purpose is the definition and identification of clinically actionable alterations. A heavy investment in human curation, of a clinical rather than exclusively scientific nature is needed to make them valuable, but each knowledgebase
Fichier principal
Vignette du fichier
Cambrosio2020_Chapter_OvercomingTheBottleneckKnowled.pdf (831.23 Ko) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-03192959 , version 1 (08-04-2021)

Licence

Paternité

Identifiants

Citer

Alberto Cambrosio, Jonah Campbell, Etienne Vignola-Gagné, Peter Keating, Bertrand R Jordan, et al.. ‘Overcoming the Bottleneck’: Knowledge Architectures for Genomic Data Interpretation in Oncology. pp.305 - 327, 2020, ⟨10.1007/978-3-030-37177-7_16⟩. ⟨hal-03192959⟩
67 Consultations
106 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More