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ancestrydna-to-vcf

Contains Public Ancestry DNA Raw File, Ancestry Conversion Code, and the .vcf Converted Output File

bhuvan-s_utilities

cad_meta_prediction

Machine learning pipeline for meta-prediction of coronary artery disease

cardiomyopathy_geomxdsp

carotid.art.dis.scrna_seq

Code Files For Analyzing 3 Patient Carotid Artery Single Cell RNA-Seq Experiment

cec

R scripts for circulating endothelial project

charts

dsc80-fa19

DSC 80 Fall 19 Public repo

fpr

Functional Programming in R

frequency_analyzer

A command-line tool for isolating frequencies found in accelerometer data.

gadget-pipeline

An analysis pipeline used to classify ET vs. PD tremor based on wrist-mounted accelerometer data.

genome-tiling

A Set of R Tools for Splitting up Chromosomes into Regions of High and Low Recombination

genomic.kernels

Kernels for Genomic Data

genotype_imputation_pipeline

Final version of the pipeline for processing TOPMED and other dbGaP datasets, includes: automatic identification of reference genome build and selection of correct chain file, mapping to GRCh37 reference build, initial quality control, ancestry analysis, post-ancestry quality control, phasing, imputation, post-imputation quality control.

imputation_accuracy_calculator

imputation_autoencoder

Deep learning algorithm for imputation of genetic variants

imputator_inference

lohrasb

This versatile tool streamlines hyperparameter optimization in machine learning workflows.

metapipe

A pipeline generator and runtime system

ncbi-scraper

A python library for parsing data from NCBI website

python_data_science_internship

Python learning for data science on analyzing the speed and accuracy of the imputation model (autoencoder)

rna_seq

script to run rna seq tools

scripps-auth

A simple key-pair-based token authentication library

sg-adviser_ui

slack-hubot-docker

Docker image for running Hubot with Slack

sparse.cor

R package for calculating sparse correlations

zoish

Zoish is a Python package that streamlines machine learning by leveraging SHAP values for feature selection and interpretability, making model development more efficient and user-friendly