Cargando…
Converting tabular data into images for deep learning with convolutional neural networks
Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging. However, most tabular data do not assume a spatial relationship between features, and thus are unsuitable for...
Autores principales: | Zhu, Yitan, Brettin, Thomas, Xia, Fangfang, Partin, Alexander, Shukla, Maulik, Yoo, Hyunseung, Evrard, Yvonne A., Doroshow, James H., Stevens, Rick L. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8166880/ https://www.ncbi.nlm.nih.gov/pubmed/34059739 http://dx.doi.org/10.1038/s41598-021-90923-y |
Ejemplares similares
-
Publisher Correction: Converting tabular data into images for deep learning with convolutional neural networks
por: Zhu, Yitan, et al.
Publicado: (2021) -
Ensemble transfer learning for the prediction of anti-cancer drug response
por: Zhu, Yitan, et al.
Publicado: (2020) -
Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models
por: Zhu, Yitan, et al.
Publicado: (2020) -
Learning curves for drug response prediction in cancer cell lines
por: Partin, Alexander, et al.
Publicado: (2021) -
Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images
por: Partin, Alexander, et al.
Publicado: (2023)