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Jerome's Projects

apricot icon apricot

apricot implements submodular optimization for the purpose of selecting subsets of massive data sets to train machine learning models quickly. See the documentation page: https://apricot-select.readthedocs.io/en/latest/index.html

basenji icon basenji

Sequential regulatory activity predictions with deep convolutional neural networks.

bolt icon bolt

10x faster matrix and vector operations

cadd-scripts icon cadd-scripts

CADD scripts release for offline scoring. For more information about CADD, please visit our website

coloc icon coloc

Repo for the R package coloc

csc509 icon csc509

For the CSC509 SFSU Course Deep Learning in Medical Imaging

dgn2vec icon dgn2vec

Pathogenic gene prediction based on network embedding

dyvider icon dyvider

Dynamic programming algorithms for exact linear clustering in networks.

eclipser icon eclipser

Enrichment of Causal Loci and Identification of Pathogenic cells in Single Cell Expression and Regulation data

enformer-pytorch icon enformer-pytorch

Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch

finemapping-insights icon finemapping-insights

Code for Kanai, M. et al. Insights from complex trait fine-mapping across diverse populations. (2021)

gatmda icon gatmda

Graph attention networks-based framework for human microbe-disease association prediction

gget icon gget

🧬 gget enables efficient querying of genomic reference databases

gwaslab icon gwaslab

A Python package for handling and visualizing GWAS summary statistics. https://cloufield.github.io/gwaslab/ https://gwaslab.com/python-gwaslab/

hail icon hail

Scalable genomic data analysis.

ml-pitfalls icon ml-pitfalls

Notebooks accompanying the paper "Navigating the pitfalls of applying machine learning in genomics"

polyfun icon polyfun

PolyFun (POLYgenic FUNctionally-informed fine-mapping)

pyblip icon pyblip

Bayesian Linear Programming for Resolution-Adaptive Signal Detection

pygwasvcf icon pygwasvcf

Python library for reading GWAS summary statistics in GWAS-VCF files

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