Fully-Connected Neural Networks with Reduced Parameterization for Predicting Histological Types of Lung Cancer from Somatic Mutations

Several challenges appear in the application of deep learning to genomic data. First, the dimensionality of input can be orders of magnitude greater than the number of samples, forcing the model to be prone to overfitting the training dataset. Second, each input variable’s contribution to the predic...

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Detalles Bibliográficos
Autores principales: Kobayashi, Kazuma, Bolatkan, Amina, Shiina, Shuichiro, Hamamoto, Ryuji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7563438/
https://www.ncbi.nlm.nih.gov/pubmed/32872133
http://dx.doi.org/10.3390/biom10091249

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7563438/
https://www.ncbi.nlm.nih.gov/pubmed/32872133
http://dx.doi.org/10.3390/biom10091249

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