seqgra: principled selection of neural network architectures for genomics prediction tasks

Abstract Motivation: Sequence models based on deep neural networks have achieved state-of-the-art performance on regulatory genomics prediction tasks, such as chromatin accessibility and transcription factor binding. But despite their high accuracy, their contributions to a mechanistic understandi...

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Detalhes bibliográficos
Main Authors: Krismer, Konstantin, Hammelman, Jennifer, Gifford, David K
Outros Autores: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Formato: Artigo
Idioma:English
Publicado em: Oxford University Press (OUP) 2022
Acesso em linha:https://hdl.handle.net/1721.1/143575