Explore projects
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BDS / AD sex-specific differences
GNU General Public License v3.0 or laterUpdated -
genomeanalysis / CNV_GWAS_COURAGE-PD
MIT LicenseUpdated -
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Miroslav Kratochvil / minervaR
GNU General Public License v3.0 onlyR code for an interface to MINERVA API and helper functions
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lsru_bionfo / pipeline_16s
MIT LicenseUpdated -
LCSB-BioCore / publications / KratochvilWilken24 - COBREXA2
Apache License 2.0Updated -
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BDS / sccca
GNU General Public License v3.0 onlyUpdated -
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LCSB-BioCore / publications / Pavelka23-miRNA_PD_PSP_LuxPark
Apache License 2.0Updated -
Elisa Gomezdelope / ML_UPDRSIII_metab_transc
MIT LicenseThis 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.
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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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Mohamed Soudy / ScRNAIMM
GNU General Public License v3.0 or laterUpdated -
Elisa Gomezdelope / GRL_sample_similarity_PD
MIT LicenseGraph 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.
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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.
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