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

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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Bibliographic Details
Main Authors: Kazuma Kobayashi, Amina Bolatkan, Shuichiro Shiina, Ryuji Hamamoto
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:Biomolecules
Subjects:
deep learning
Diet Networks
lung cancer
interpretable neural networks
Online Access:https://www.mdpi.com/2218-273X/10/9/1249

Internet

https://www.mdpi.com/2218-273X/10/9/1249

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