Deep learning integrates histopathology and proteogenomics at a pan-cancer level

Keyphrases

• Histology 100%
• Proteomics 100%
• Deep Learning 100%
• Pan-cancer 100%
• Transcriptomics 100%
• Area under the Receiver Operating Characteristic (AUROC) 100%
• Proteogenomics 100%
• Cellular Processes 50%
• In Cancer 50%
• Clinical Setting 50%
• Tumor 50%
• Clinical Outcomes 50%
• Pathologist 50%
• Publicly Available 50%
• Cancer Types 50%
• Pathological Stage 50%
• Predictive Power 50%
• Tumor Protein p53 (TP53) 50%
• Classification System 50%
• Machine Learning Approach 50%
• Potential Clinical Applications 50%
• Tissue of Origin 50%
• Web Platform 50%
• Histopathological Images 50%
• Histological Slides 50%
• Model Generalization 50%
• Model Interpretability 50%
• CPTAC 50%
• Sign Process 50%
• Human Learning 50%

Medicine and Dentistry

• Malignant Neoplasm 100%
• Histopathology 100%
• Transcriptomics 100%
• Proteomics 100%
• Proteogenomics 100%
• Neoplasm 50%
• Pathologist 50%
• Cancer Types 50%
• The Cancer Genome Atlas 50%

Biochemistry, Genetics and Molecular Biology

• Transcriptomics 100%
• Proteomics 100%
• Proteogenomics 100%
• Pan-cancer 100%