ccb-hms/nhanes-database

Stars
4
Rank 3,304,323 (Top 66 %)
Language
R
Created over 3 years ago
Updated 7 months ago

ccb-hms/nhanes-database

ccb-hms

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

ontology-mapper

a tool for mapping free-text descriptions of entities to ontology terms

BioPlex

R-side access to PPI data from Gygi lab

BioPlexAnalysis

Applications and downstream analysis of BioPlex PPI data

docker-rstudio-server

A repository for RStudio Server containers, with the goal of providing multi-architecture (ARM64 and AMD64) images.

HarvardInovalonUserGuide

User guide for Harvard's Inovalon resources.

computervision

Everything to get started with computer vision

Jupyter Notebook

phonto

PHenome - PHenome ONTOlogy for NHANES

docker-analytic-workbench

scDiagnostics

Diagnostic functions to assess the quality of cell type annotations in single-cell RNA-seq data

BioPlexPy

Python-side access to PPI data from Gygi lab

Jupyter Notebook

gwasCatSearch

tooling for semantic search of NHGRI GWAS catalog

NHANES-metadata

scConform

Uncertainty quantification for cell type annotation using conformal inference

osca-workbench

Single-cell RNAseq materials derived from the 2023 ISMB OSCA workshop in the carpentries workbench format.

HuBMAPy

A package to query the HuBMAP Human Reference Atlas Ontology

systems-immunology-workshops

2023 Workshops to Accompany Systems Immunology Course

hsdm-r-course

Website for materials for the R component of HSDM's Research Design and Analysis Course

CyCIFData

Collection of public CyCIF datasets

hmc-clinic-2022-23

HMC clinic project 2022/23

Jupyter Notebook

nhanes-exploration

Exploring NHANES data through the nhanesA package

workbench-python-workshop

8-hour workshop introducing Python using The Carpentries new workbench template.

MerfishData

Collection of public MERFISH datasets

MouseAgingData

Mouse aging multi-omics project with Rubin Lab

unbalanced-data-example

This repository contains code to illustrate the effects of unbalanced data on classification models, including poor optimization, Hessian matrix issues, and biased model performance.