LamineTourelab

MOGONET

MOGONET (Multi-Omics Graph cOnvolutional NETworks) is multi-omics data integrative analysis framework for classification tasks in biomedical applications.

Tutorial

Tutorials on machine learning, artificial intelligence in general and in biomedical research.

AI-for-medicine-specialization

Medical Images processing

Explainable-AI

In this repository you will fine explainability of machine learning models.

OmiVAE

End-to-end deep learning model for low dimensional latent space extraction and multi-class classification on multi-omics datasets.

SingleCell-rnaseq-tools

This repository contains single cell analysis pipelines.

grpregOverlap

Regularization paths of linear, logistic, Poisson, or Cox models with overlapping grouped covariates

ML-For-Beginners

12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

awesome-computer-vision

A curated list of awesome computer vision resources

awesome-multi-omics

List of software packages for multi-omics analysis

dcai-lab

Lab assignments for Introduction to Data-Centric AI, MIT IAP 2023 👩🏽‍💻

LamineTourelab.github.io

lime

Lime: Explaining the predictions of any machine learning classifier

Machine-Learning-with-Graphs

Graphs are a general language for describing and analyzing entities with relations/interactions.

NetICS-multi-omics

NetICS: network-based integration of multi-omics data for prioritizing cancer genes

shapash

🔅 Shapash makes Machine Learning models transparent and understandable by everyone

Streamlit

Streamlit App with restoration images algorithm (on going)

Deep-Learning-with-TensorFlow-and-Keras-3rd-edition

Deep Learning with TensorFlow and Keras – 3rd edition, Published by Packt

gcp-for-bioinformatics

GCP for Bioinformatics Researchers

Interpretable-Machine-Learning-with-Python

Interpretable Machine Learning with Python, published by Packt

interpretable-ml-book

Book about interpretable machine learning

LamineTourelab

machine-learning-book

Code Repository for Machine Learning with PyTorch and Scikit-Learn