A database for microRNA-gene/protein expression correlation in cancer.

CONICS: COpy-Number analysis In single-Cell RNA-Sequencing

Build the database file for cRegulome package

Pan-cancer analysis of metastatic samples

Creating a classifier for bladder cancer using Elastic Net Regression

Creating a Random Forest model to predict the progression of bladder cancer

Six machine learning models for predicting bladder cancer progression using Caret package

Mechanistic Inference of Node-Edge Relationships (MINER)

An R package to access, manage and visualize regulome (microRNA/transcription factors)-gene correlations in cancer

Predicting chemosensitivity using gene expression

Advanced plotting functions for CNV data generated by CNV-Seq and Control Freec

BPSI Project 2020 applying machine learning to regulators of patient outcome ER in breast cancer.

Scripts and data for reproducing analysis in Miller et al. 2020

Callable Cancer Loci - assessment of sequencing coverage for actionable and pathogenic loci in cancer

Bayesian inference of clone-specific gene expression estimates by integrating single-cell RNA-seq and single-cell DNA-seq data

This repository contains all machine learning and statistical models used to analyze the landscape of colorectal cancer.

R Code and Required Files for Ma 2020 submission

Assessing dIfferential expression, dispersion, distribution analyses on TCGA tumour-normal RNA-seq datasets

A tidyverse-style package for plotting mutational signatures and context counts.