Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks

Deep neural networks have demonstrated improved performance at predicting the sequence specificities of DNA- and RNA-binding proteins compared to previous methods that rely on k-mers and position weight matrices. To gain insights into why a DNN makes a given prediction, model interpretability method...

Descripción completa

Detalles Bibliográficos
Autores principales: Koo, Peter K., Majdandzic, Antonio, Ploenzke, Matthew, Anand, Praveen, Paul, Steffan B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8118286/
https://www.ncbi.nlm.nih.gov/pubmed/33983921
http://dx.doi.org/10.1371/journal.pcbi.1008925

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8118286/
https://www.ncbi.nlm.nih.gov/pubmed/33983921
http://dx.doi.org/10.1371/journal.pcbi.1008925

Ejemplares similares