Clasterization of TCGA dataset. Data preprocessing, visualization and clasterization with different alghoritms. Done mostly with Python 3.7 and Scikit-Learn library.
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Updated
Dec 13, 2020 - Jupyter Notebook
Clasterization of TCGA dataset. Data preprocessing, visualization and clasterization with different alghoritms. Done mostly with Python 3.7 and Scikit-Learn library.
Use this program can let user quickly combine the miRNA/Gene expression and clinical data. Speed up the data preprocessing step.
BSc thesis: "Convolutional Neural Networks and their Application in Cancer Diagnosis based on RNA-Sequencing"
SOPHYSM - SOlid tumors PHYlogenetic Spatial Modeller - Julia GUI for Histological Analysis and Cancer Simulation
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.
Tumor2Graph: a novel Overall-Tumor-Profile-derived virtual graph deep learning for predicting tumor typing and subtyping.
GNU Make-driven workflow to download TCGA data via the TCGAbiolinks package
XGEP for expression-based prediction of human essential genes and candidate lncRNAs in cancer cells
Supplementary R scripts for the manuscript "Disrupting PGE2/EP3 signaling in cancer-associated fibroblasts limits mammary carcinoma growth but promotes metastasis"
AI HUB Project: Classification of Lung Cancer Slide Images using Deep-Learning
A Community Curated Immune Resource Database in R format
Python script to integrate files downloaded from the GDC server and normalized.
Genomic Data Commons Query
Scripts to manage biotab files from TCGA.
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