Explore projects
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COVID-19 / models
GNU General Public License v3.0 onlyComputational models of different aspects of COVID-19
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elixir / beacon
Apache License 2.0ELIXIR Luxembourg's GA4GH Beacon API implementation in Python
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SMASCH / scheduling-system
GNU Affero General Public License v3.0Scheduling assignments for Parkinson Research Clinic
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R3 / outreach / templates / presentations / markdown
Creative Commons Zero v1.0 UniversalUpdated -
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Environmental Cheminformatics / pubchem
Artistic License 2.0A project for interactions with PubChem
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IMP / IMP3
MIT LicenseUpdated -
ESB / CO-INFECTOMICS
MIT LicenseCO-INFECTOMICS - identification of co-infections and other factors associated with COVID-19 severity in the gut microbiome
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genomeanalysis / LuxPARK
Apache License 2.0Updated -
Code used for the analysis of the RNA-seq, ATAC-seq and ChIP-seq datasets produced in this study. Original fastq files deposited in https://ega-archive.org/, under the accession number EGAD00001009288.
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Laura Denies / PathoFact
GNU General Public License v3.0 or laterUpdated -
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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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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Elisa Gomezdelope / basic-practice-pages
MIT LicenseBasic practice repository for git trainings
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