Marc-André Legault (legaultmarc)

legaultmarc

Geek Repo

Company:Mila / McGill

Location:Montréal

Home Page:http://www.marclegault.com/

Twitter:@legaultmarc

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pgxcentre

Marc-André Legault's starred repositories

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bio2zarr

Convert bioinformatics file formats to Zarr

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boostIV

Causal Gradient Boosting: Boosted Instrumental Variable Regression

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restic

Fast, secure, efficient backup program

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gwas-summary-statistics-standard

Documentation on new GWAS Summary Statistics Standard

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pycox

Survival analysis with PyTorch

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gwaslab

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

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FriendsDontLetFriends

Friends don't let friends make certain types of data visualization - What are they and why are they bad.

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ml-stable-diffusion

Stable Diffusion with Core ML on Apple Silicon

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DomainBed

DomainBed is a suite to test domain generalization algorithms

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cookiecutter

A cross-platform command-line utility that creates projects from cookiecutters (project templates), e.g. Python package projects, C projects.

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magenpy

Modeling and Analysis of (Statistical) Genetics data in python

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1000-genomes-genetic-maps

Genetic maps interpolated to sites in the 1000 Genomes project

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The-Art-of-Linear-Algebra

Graphic notes on Gilbert Strang's "Linear Algebra for Everyone"

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viprs

Variational Inference of Polygenic Risk Scores

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pgs-calc

Applying polygenic scores (PGS) on imputed genotypes

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InteractiveComplexHeatmap

Make Interactive Complex Heatmaps

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transfertools

Python toolbox for transfer learning.

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Kalman-and-Bayesian-Filters-in-Python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

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annotated_latex_equations

Examples of how to create colorful, annotated equations in Latex using Tikz.

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pytorch-lasso

L1-regularized least squares with PyTorch

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manim

Animation engine for explanatory math videos

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pcsstools

Tools for regression using pre-computed summary statistics

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CPA

The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates.

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sbi

Simulation-based inference toolkit

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dbeaver

Free universal database tool and SQL client

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dopamine

Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.

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