Tools for producing pseudo-cgh of next-generation sequencing data
-
Updated
Sep 5, 2016 - Python
Tools for producing pseudo-cgh of next-generation sequencing data
A prototype for the Exceptional Responder tool.
Network-based Cancer Drug Feature Selection and Modeling
Precision Medicine Target-Drug Selection in Cancer
Visualizing TCGA pancancer datasets
Open-source command-line pipeline for cancer type classification of high-throughput data using machine learning.
Various utility scripts and ICGC DCC data specification templates / conversion tools written during early development work on the current ICGC DCC data portal (2.0)
Data & Scripts for the Memorial Sloan Kettering Cancer Center's (MSKCC) request for a machine learning algorithm that, using annotated information on genomic variants, automatically classifies genetic variations as either neutral or cancerous.
Downloading and processing pipelines used for single cell gene expression assays of human tumor biopsies
My lab book for current project: Identifying structural variation in WGS data
This web application allows the user to explore interactively data from a cohort of 113 myeloproliferative neoplasm (MPN) blood donors and 15 healthy blood controls
Public Multi-omics Resources for Translational Medicine Research
LEGACY: AutoCSA (Automatic Comparative Sequence Analysis) is a mutation detection program designed to detect small mutations (1-50 bases) in capillary sequence traces. The software is capable of detecting both homozygous and heterozygous base substitutions, as well as small insertions and deletions, to a high sensitivity.
Python script to download files from the GDC server
A more powerful analysis of tumor heterogeneity
CONICS: COpy-Number analysis In single-Cell RNA-Sequencing
Classify Cancer types upon Alleles
Add a description, image, and links to the cancer-genomics topic page so that developers can more easily learn about it.
To associate your repository with the cancer-genomics topic, visit your repo's landing page and select "manage topics."