Application of neural network autoencoder algorithm in the cancer informatics research / 生物工程学报
Chinese Journal of Biotechnology
;
(12): 2393-2404, 2021.
Artigo
em Chinês
| WPRIM
| ID: wpr-887805
ABSTRACT
Cancers have been widely recognized as highly heterogeneous diseases, and early diagnosis and prognosis of cancer types have become the focus of cancer research. In the era of big data, efficient mining of massive biomedical data has become a grand challenge for bioinformatics research. As a typical neural network model, the autoencoder is able to efficiently learn the features of input data by unsupervised training method and further help integrate and mine the biological data. In this article, the primary structure and workflow of the autoencoder model are introduced, followed by summarizing the advances of the autoencoder model in cancer informatics using various types of biomedical data. Finally, the challenges and perspectives of the autoencoder model are discussed.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Algoritmos
/
Redes Neurais de Computação
/
Informática
/
Neoplasias
Tipo de estudo:
Estudo prognóstico
/
Estudo de rastreamento
Limite:
Humanos
Idioma:
Chinês
Revista:
Chinese Journal of Biotechnology
Ano de publicação:
2021
Tipo de documento:
Artigo
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