DNN-PNN: A parallel deep neural network model to improve anticancer drug sensitivity.
Methods
; 209: 1-9, 2023 01.
Article
en En
| MEDLINE
| ID: mdl-36410694
ABSTRACT
With the rapid development of deep learning techniques and large-scale genomics database, it is of great potential to apply deep learning to the prediction task of anticancer drug sensitivity, which can effectively improve the identification efficiency and accuracy of therapeutic biomarkers. In this study, we propose a parallel deep learning framework DNN-PNN, which integrates rich and heterogeneous information from gene expression and pharmaceutical chemical structure data. With the proposal of DNN-PNN, a new and more effective drug data representation strategy is introduced, that is, the correlation between features is represented by product, which alleviates the limitations of high-dimensional discrete data in deep learning. Furthermore, the framework is optimized to reduce the time complexity of the model. We conducted extensive experiments on the CCLE datasets to compare DNN-PNN with its variant DNN-FM representing the traditional feature correlation model, the component DNN or PNN alone, and the common machine learning models. It is found that DNN-PNN not only has high prediction accuracy, but also has significant advantages in stability and convergence speed.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Redes Neurales de la Computación
/
Antineoplásicos
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Año:
2023
Tipo del documento:
Article