PathCNN: interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma is a research paper published in Bioinformatics (2021). On theSindex it has a DataRank of 0. It has been cited 66 times.
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National Cancer Institute Cancer Center
Grant: P30 CA008748
National Cancer Institute Cancer Center
Grant: R21 CA234752
Air Force Office of Sponsored Research
Grant: FA9550-17-1-0435
Air Force Office of Sponsored Research
Grant: FA9550-20-1-0029
Breast Cancer Research Foundation
Grant: BCRF-17-193
Ministry of Science
Grant: ICT 2019-0-01601
National Institutes of Health
Grant: 5R21CA234752-02
Radiotherapy-associated breast cancer: machine learning on genotypes to predict individualized risk
National Institutes of Health
Grant: 2P30CA008748-43
MOUSE GENETICS
National Institutes of Health
NIH HHS
FWCI
5.24
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Additional file 1 of Risk stratification and pathway analysis based on graph neural network and interpretable algorithm
Additional file 1 of Risk stratification and pathway analysis based on graph neural network and interpretable algorithm
Additional file 2 of Risk stratification and pathway analysis based on graph neural network and interpretable algorithm
Additional file 2 of Risk stratification and pathway analysis based on graph neural network and interpretable algorithm
Additional file 3 of Risk stratification and pathway analysis based on graph neural network and interpretable algorithm
Additional file 3 of Risk stratification and pathway analysis based on graph neural network and interpretable algorithm
Additional file 2 of A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data
Additional file 2 of A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data
Additional file 1 of A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data
Additional file 1 of A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data