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Computational models of different aspects of COVID-19

Integrated meta-omics pipeline

An R package for generating chemical stripes from patent or literature data extracted from PubChem

Additional analyses for IMP manuscript

LAO time series analyses

seesaw_code project, see details in:

A project for interactions with PubChem

Repository for NORMAN-SLE work organization at ECI, including list tracking, updates and documentation. Main representatives: Emma and Hiba.

Repository for scripts used in the analyses for the publication "Integration of time-series meta-omics data reveals how microbial ecosystems respond to disturbance"

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.

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

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 is a mirror from https://github.com/BarlierC/RNetDys.

This 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.

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 CCL