Deep Neural Networks for Epistatic Sequence Analysis.
Methods Mol Biol
; 2212: 277-289, 2021.
Article
em En
| MEDLINE
| ID: mdl-33733362
ABSTRACT
We report a step-by-step protocol to use pysster, a TensorFlow-based package for building deep neural networks on a broad range of epistatic sequences such as DNA, RNA, or annotated secondary structure sequences. Pysster provides users comprehensive supports for developing, training, and evaluating the self-defined deep neural networks on sequence data. Moreover, pysster allows users to easily visualize the resulting perditions, which is helpful to uncover the "black box" of deep neural networks. Here, we describe a step-by-step application of pysster to classify the RNA A-to-I editing regions and interpret the model predictions. To further demonstrate the generalizability of pysster, we utilized it to build and evaluated a new deep neural network on an artificial epistatic sequence dataset.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Software
/
RNA
/
Epistasia Genética
/
Aprendizado Profundo
/
Modelos Genéticos
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Methods Mol Biol
Assunto da revista:
BIOLOGIA MOLECULAR
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Hong Kong