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

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...

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Autores principales: 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
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Medicine
R
Science
Q
Acceso en línea:https://doaj.org/article/1e3b3aca06414847b486a6e05de1d438
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https://doaj.org/article/1e3b3aca06414847b486a6e05de1d438

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