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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.

This repository contains the code for statistical analyses performed in Chapter 3 of my thesis "Cross-sectional and longitudinal profiling of PD transcriptomics and metabolomics". The project consists on whole blood transcriptomics and blood plasma metabolomics cross-sectional and longitudinal profiling of Parkinson's disease patients and controls from the PPMI cohort and the LuxPARK cohort respectively, to identify differential molecular and higher-level functional features in PD.

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

Code related to Talavera Andujar et al DOI: 10.26434/chemrxiv-2023-6j2gm

ShinyTPs: Curate transformation products from text mining results from HSDB & PubChem

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.