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Graph representation learning modelling pipeline exploiting molecular interaction networks of transcriptomics (protein-protein interactions) and metabolomics (metabolite-metabolite interactions) to learn PD-specific fingerprints from the spatial distribution of molecular relationships in an end-to-end fashion. The scripts apply the graph representation learning modelling pipeline on networks of molecular interactions, where transcriptomics and metabolomics data from the PPMI and the LuxPARK cohort, respectively, are projected.

This repository contains the code for ML analyses performed in Chapter 4 of my PhD thesis "Interpretable Machine Learning on omics data for biomarker discovery in Parkinson's disease". The project consists on performing Parkinson's disease (PD) case-control classification from blood plasma metabolomics measurements at the baseline clinical visit from the LuxPARK cohort, and from whole blood transcriptomics data at baseline as well as dynamic features engineered from a short temporal series of 4 timepoints from the PPMI cohort. The study involves evaluation of different feature selection strategies, The goal was to build and test a collection of ML models and, most interestingly, identify molecular and higher-level functional representations associated with PD diagnosis.

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.

A package and environment with the NestedCV() class - a scikit-learn compatible class for nested cross-validation.

Repository for all slides related to R3 courses.

This project hosts the JSON schemas used for representing the metadata of submissions to the ELIXIR translational data repository. It also provides validation utility methods.

This project hosts the JSON schemas used for representing the metadata of submissions to the ELIXIR translational data repository. It also provides validation utility methods.

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.

Basic practice repository for git trainings

Basic practice repository for git trainings

Here we share scripts using MINERVA API

Repository for building the official website of the R3 lab.

Here is the presence on the university website.

Repository for all slides related to R3 courses.

Repository for all slides related to R3 courses.

Basic practice repository for git trainings