Pinned Repositories
arboreto
A scalable python-based framework for gene regulatory network inference using tree-based ensemble regressors.
bigwigfile
bioinformatics
:microscope: Path to a free self-taught education in Bioinformatics!
Breast-Cancer-Prediction
A machine learning process to distinguish good from bad breast cancer.
chipseq
ChIP-seq peak-calling, QC and differential analysis pipeline.
Clinical-Decision-Support-using-Machine-Learning
Predicting the Stage of Breast Cancer - M (Malignant) and B (Benign) using different Machine learning models and comparing their performance.
GCB535
Materials for GCB535 at Penn.
Homework_2020
Homeworks
Intro-to-ChIPseq
Intro to ChIPseq using HPC
MAESTRO
Single-cell Transcriptome and Regulome Analysis Pipeline
rb56's Repositories
rb56/arboreto
A scalable python-based framework for gene regulatory network inference using tree-based ensemble regressors.
rb56/bigwigfile
rb56/bioinformatics
:microscope: Path to a free self-taught education in Bioinformatics!
rb56/Breast-Cancer-Prediction
A machine learning process to distinguish good from bad breast cancer.
rb56/chipseq
ChIP-seq peak-calling, QC and differential analysis pipeline.
rb56/Clinical-Decision-Support-using-Machine-Learning
Predicting the Stage of Breast Cancer - M (Malignant) and B (Benign) using different Machine learning models and comparing their performance.
rb56/GCB535
Materials for GCB535 at Penn.
rb56/Homework_2020
Homeworks
rb56/Intro-to-ChIPseq
Intro to ChIPseq using HPC
rb56/MAESTRO
Single-cell Transcriptome and Regulome Analysis Pipeline
rb56/py4e
Web site for www.py4e.com and source to the Python 3.0 textbook
rb56/pySCENIC
pySCENIC is a lightning-fast python implementation of the SCENIC pipeline (Single-Cell rEgulatory Network Inference and Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from single-cell RNA-seq data.
rb56/rnaseq_tutorial
Informatics for RNA-seq: A web resource for analysis on the cloud. Educational tutorials and working pipelines for RNA-seq analysis including an introduction to: cloud computing, critical file formats, reference genomes, gene annotation, expression, differential expression, alternative splicing, data visualization, and interpretation.
rb56/SCENIC
SCENIC is an R package to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data.
rb56/training-material
A collection of Galaxy-related training material