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.
Article
Dans Chinois
| 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.
Texte intégral:
Disponible
Indice:
WPRIM (Pacifique occidental)
Sujet Principal:
Algorithmes
/
/
Informatique
/
Tumeurs
Type d'étude:
Étude pronostique
/
Étude de dépistage
Limites du sujet:
Humains
langue:
Chinois
Texte intégral:
Chinese Journal of Biotechnology
Année:
2021
Type:
Article
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