Explore projects
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ELIXIR Luxembourg's GA4GH Beacon API implementation in Python
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CO-INFECTOMICS - identification of co-infections and other factors associated with COVID-19 severity in the gut microbiome
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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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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.
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Method testing and analyses of Oxford Nanopore Technology (ONT) sequencing runs Data obtained by sequencing "generous donor B" across several runs Original data prepared by Rashi (post-sequencing) Initial analyses performed by CCLUpdated
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Graph 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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