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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.

Repository for all slides related to R3 courses.

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.

Client for GA4GH Data Repository Service (DRS) API service

Basic practice repository for git trainings

Basic practice repository for git trainings

Basic practice repository for git trainings. Carmen's fork.

Basic practice repository for git trainings

Basic practice repository for git trainings

Gene regulation inference of COVID-19

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 includes the generator scripts for generating the index files of the howto-cards and modules (handbook, qms, ...)

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.

Benchmark scripts related to the COBREXA.jl publication

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.