ExplaiNN: interpretable and transparent neural networks for genomics

Deep learning models such as convolutional neural networks (CNNs) excel in genomic tasks but lack interpretability. We introduce ExplaiNN, which combines the expressiveness of CNNs with the interpretability of linear models. ExplaiNN can predict TF binding, chromatin accessibility, and de novo motif...

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Detalles Bibliográficos
Autores principales: Novakovsky, Gherman, Fornes, Oriol, Saraswat, Manu, Mostafavi, Sara, Wasserman, Wyeth W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10303849/
https://www.ncbi.nlm.nih.gov/pubmed/37370113
http://dx.doi.org/10.1186/s13059-023-02985-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10303849/
https://www.ncbi.nlm.nih.gov/pubmed/37370113
http://dx.doi.org/10.1186/s13059-023-02985-y

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