Raíssa Silva

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.

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.

geneRFinder: gene finding in distinct metagenomic data complexities

geneRFinder is a machine learning-based gene predictor that outperforms existing tools on metagenomic data, with significant improvements in prediction rates and specificity.

PIMBA: a PIpeline for MetaBarcoding Analysis

PIMBA is a pipeline for DNA metabarcoding analysis that supports custom databases and integrates multiple reference databases into a user-friendly, open-source tool.