Verifying explainability of a deep learning tissue classifier trained on RNA-seq data

Abstract For complex machine learning (ML) algorithms to gain widespread acceptance in decision making, we must be able to identify the features driving the predictions. Explainability models allow transparency of ML algorithms, however their reliability within high-dimensional data is unclear. To t...

Description complète

Enregistré dans:
Détails bibliographiques
Auteurs principaux: Melvyn Yap, Rebecca L. Johnston, Helena Foley, Samual MacDonald, Olga Kondrashova, Khoa A. Tran, Katia Nones, Lambros T. Koufariotis, Cameron Bean, John V. Pearson, Maciej Trzaskowski, Nicola Waddell
Format: article
Langue:EN
Publié: Nature Portfolio 2021
Sujets:
R
Q
Accès en ligne:https://doaj.org/article/1e3b3aca06414847b486a6e05de1d438
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!