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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.
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Carmen Lahr / basic-practice-pages
MIT LicenseBasic practice repository for git trainings. Carmen's fork.
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Jenny Thuy Dung Tran / basic-practice-pages
MIT LicenseBasic practice repository for git trainings
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Janine Schulz / basic-practice-pages
MIT LicenseBasic practice repository for git trainings
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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.
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Environmental Cheminformatics / PD cheminformatics pipeline
Artistic License 2.0Updated -
Computational modelling and simulation / GeneRegulationAnalysis
GNU Affero General Public License v3.0Gene regulation inference of COVID-19
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DVB / Gomez-Giro_2019
Apache License 2.0Updated -
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R3 / apps / tailorbird / linkchecker
Apache License 2.0Updated -
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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.
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R3 / apps / generator
Apache License 2.0This project includes the generator scripts for generating the index files of the howto-cards and modules (handbook, qms, ...)
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Elisa Gomezdelope / ML_PD_metab_transc
MIT LicenseThis 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.
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