Open source tools for computational pathology - Nature BME
Whole Slide Image segmentation with weakly supervised multiple instance learning on TCGA | MICCAI2020 https://arxiv.org/abs/2004.05024
Solid Tumor Associative Modeling in Pathology
Prognostically Relevant Subtypes and Survival Prediction for Breast Cancer Based on Multimodal Genomics Data
Demographic bias in misdiagnosis by computational pathology models - Nature Medicine
Tumor2Graph: a novel Overall-Tumor-Profile-derived virtual graph deep learning for predicting tumor typing and subtyping.
The official implementations of our BIBM'24 paper: Focus on Focus: Focus-oriented Representation Learning and Multi-view Cross-modal Alignment for Glioma Grading
XGEP for expression-based prediction of human essential genes and candidate lncRNAs in cancer cells
Use this program can let user quickly combine the miRNA/Gene expression and clinical data. Speed up the data preprocessing step.
The Cancer Genome Atlas (TCGA), a cancer genomics reference program, has molecularly characterized more than 20,000 primary cancer samples and paired normal samples covering 33 types of cancer. This joint effort between the NCI and the National Human Genome Research Institute began in 2006.
With this ShinyApp (BetApp), you will be able to easily visualize your data including beta values from methylation arrays downloaded using the TCGA database.
Laboratory Scientist can find a correlation between gene expressions in cancer tissue. The data can be downloaded via https://portal.gdc.cancer.gov/projects/TCGA-CHOL.
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