Project to study proliferation and metabolic liabilities across several thousand cancer samples.
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Updated
Apr 24, 2017 - R
Project to study proliferation and metabolic liabilities across several thousand cancer samples.
R package to parse and preprocess the Surveillance, Epidemiology, and End Results (SEER) Program data from NIH/NCI
Data and code for the Life Sciences in Space Research 2019 paper. Concerns modeling murine Harderian gland tumorigenesis induced by mixed radiation fields.
Creating a classifier for bladder cancer using Elastic Net Regression
Creating a Random Forest model to predict the progression of bladder cancer
Using Keras to build a deep neural network for bladder cancer progression
Six machine learning models for predicting bladder cancer progression using Caret package
Predicting chemosensitivity using gene expression
Scripts and data for reproducing analysis in Miller et al. 2020
Cancer breakpoints hotspots prediction
This repository contains all machine learning and statistical models used to analyze the landscape of colorectal cancer.
Companion for the 2022 manuscript in Cancer Epidemiology, Biomarkers & Prevention entitled "A National Map of NCI-Designated Cancer Center Catchment Areas on the 50th Anniversary of the Cancer Centers Program"
Endoscopic and Pathological data extraction for various endo-pathological data extraction
Supplementary R scripts for the manuscript "Disrupting PGE2/EP3 signaling in cancer-associated fibroblasts limits mammary carcinoma growth but promotes metastasis"
Robust Decision Making Tools for Cancer Screening Models
Set of standardized functions to operate with genomic data
Implements four major subtype classifiers for high-grade serous (HGS) ovarian cancer.
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