Anthony Boureux

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.