Convolutional neural network approach to lung cancer classification integrating protein interaction network and gene expression profiles.


Journal

Journal of bioinformatics and computational biology
ISSN: 1757-6334
Titre abrégé: J Bioinform Comput Biol
Pays: Singapore
ID NLM: 101187344

Informations de publication

Date de publication:
06 2019
Historique:
entrez: 11 7 2019
pubmed: 11 7 2019
medline: 16 7 2020
Statut: ppublish

Résumé

Deep learning technologies are permeating every field from image and speech recognition to computational and systems biology. However, the application of convolutional neural networks (CCNs) to "omics" data poses some difficulties, such as the processing of complex networks structures as well as its integration with transcriptome data. Here, we propose a CNN approach that combines spectral clustering information processing to classify lung cancer. The developed

Identifiants

pubmed: 31288636
doi: 10.1142/S0219720019400079
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1940007

Auteurs

Teppei Matsubara (T)

* Department of Information Science, Faculty of Science, Toho University, Funabashi, Chiba, Japan.

Tomoshiro Ochiai (T)

† Department of Social Information Studies, Otsuma Women's University, Tokyo, Japan.

Morihiro Hayashida (M)

‡ Department of Electrical Engineering and Computer Science, National Institute of Technology, Matsue College, Shimane, Japan.

Tatsuya Akutsu (T)

§ Bioinformatics Center, Institute for Chemical Research, Kyoto University Uji, Japan.

Jose C Nacher (JC)

* Department of Information Science, Faculty of Science, Toho University, Funabashi, Chiba, Japan.

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