Thérèse Commes

The ganglioside GD3 and its Synthase (ST8SIA1) as novel senescence markers associated with osteoarthritis

This study identifies GD3 and its synthase ST8SIA1 as novel senescence markers linked to osteoarthritis progression and shows their potential as therapeutic targets.

Comparing machine learning models for predicting mutation status in Acute Myeloid Leukemia patients using RNA-seq data

The paper compares machine learning models to predict mutation status in AML patients using RNA-seq data, finding complex models achieve high accuracy for NPM1, IDH1/IDH2, and FLT3-ITD mutations.

Exploring a large cancer cell line RNA-sequencing dataset with k-mers

The paper introduces a k-mer-based method to quantify RNA sequences from large RNA-seq datasets, demonstrating its accuracy for variant RNA detection in cancer cell lines.

A k-mer based transcriptomics analysis for NPM1-mutated AML

The paper develops a k-mer based transcriptomics analysis to distinguish NPM1-mutated from non-mutated AML patients, achieving over 95% accuracy in three scenarios.

Kmerator Suite: design of specific k-mer signatures andautomatic metadata discovery in large RNA-Seq datasets.

The paper introduces Kmerator Suite, a set of tools for extracting k-mer signatures and inferring metadata from RNA-seq datasets, showing high accuracy in gene expression quantification and metadata discovery.

Detailed analysis of public RNAseq data and longnon-coding RNA: a proposed enhancement to mesenchymalstem cell characterisation

The paper analyzes public RNAseq data to identify lncRNAs specific to mesenchymal stem cells and proposes them as potential cell markers.

Exhaustive capture of biological variation in RNA-seq data through k-mer decomposition

This paper introduces DE-kupl, a k-mer-based method to capture RNA variation without a reference, identifying novel transcripts and biological events from RNA-seq data.