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

A repository for the non-target analysis of Luxembourg surface water samples led by Dagny Aurich

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 GitLab folder provides the scripts that were used in my Master Thesis Project. For any types of questions, please feel free to contact me.

Gene regulation inference of COVID-19

All scripts used for AMR and MGE analysis of the LAO dataset

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.

R code for an interface to MINERVA API and helper functions

A home for documentation, scripts etc related to PubChem efforts

An exploration on gene expression data was carried out on single-cell RNAseq analyses of bronchoalveolar lavages from nine COVID-19 patients, three moderate cases, severe case and critical cases, comparing with healthy subjects.

The sensitivity analysis of IFN1 boolean model is investigated against over expression and knockout mutations. The simulation is performed based on the ranked sensitivity values. Attractors' analysis is also performed to support the main conclusions.

Repository for all slides related to R3 courses.