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Graph representation learning modelling pipeline exploiting sample-similarity networks derived from high-throughput omics profiles to learn PD-specific fingerprints from the spatial distribution of molecular abundance similarities in an end-to-end fashion. The scripts apply the graph representation learning modelling pipeline on sample-similarity networks of transcriptomics and metabolomics data from the PPMI and the LuxPARK cohort, respectively.

Basic practice repository for git trainings

Repository for all slides related to R3 courses.

Basic practice repository for git trainings

https://r3.pages.uni.lu/howto-cards

Want to learn how to use snakemake? Here are some example to demonstrate the main components and some advanced functionalities.

A project for interactions with PubChem

Basic practice repository for git trainings

xBEL to Cell Designer convertor

An automated workflow for constructing maps of molecular mechanisms for given diseases. It streamlines querying gene-disease and variant-disease associations, calculating enrichment of disease maps and pathways, and combining enriched diagrams with text mining data into a single map, meant for the MINERVA Platform.

Integrated meta-omics pipeline

scripts for ZOTERO .bib generation

Training materials for the MINERVA Platform

This repository contains the code for ML analyses performed in Chapter 5 of my PhD thesis "Interpretable machine learning on omics data for the study of UPDRS III prognosis". The project consists on predicting the Unified Parkinson’s Disease Rating Scale Part III (UPDRS III) motor scores (mild/severe when classification) from whole blood transcriptomics and blood plasma metabolomics using measurements from the baseline clinical visit, and temporal or dynamic features engineered from a short temporal series of 4 and 3 timepoints, respectively, from the PPMI cohort and the LuxPARK cohort, aiming at identifying molecular and higher-level functional fingerprints linked specifically to the motor symptoms in PD.