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EttoreRocchi/README.md

Ettore Rocchi

About me

Physics background, biomedical mission.

I'm a PhD researcher at the University of Bologna, where I develop computational methods to predict antimicrobial resistance, discover patient phenotypes, and make sense of high-dimensional omics data. My work spans MALDI-TOF mass spectrometry, multi-omics integration, and metagenomics, always with a focus on interpretability and clinical impact. Part of the Physics4MedicineLab group and the Multi-Omics and Health-Care Data Analytics Unit at Sant'Orsola Hospital.

LinkedIn Google Scholar Scopus University Email

GitHub Stats

GitHub followers GitHub stars


What I Work On

Area Focus
ML in Biomedicine Interpretable models, feature engineering, multi-omics integration
Mass Spectrometry Predicting antimicrobial resistance from MALDI-TOF spectra
Metagenomics Network-based microbial community modeling and pathogen detection
Genomics APOBEC mutations, structural variants, CRISPR genome editing tools

Tech Stack

Python scikit-learn PyTorch Bash Snakemake Nextflow


Flagship Projects

MaldiAMRKit combatlearn ResPredAI

More Projects

Project Description
phenocluster Unsupervised clinical phenotype discovery with survival and multistate modeling
CATS Automated Cas9 nuclease comparison with ClinVar integration
CAMISIM-BrokenStick Broken stick model extension for metagenomic simulation
APOBECSeeker APOBEC-style mutation identification from multiple sequence alignment
nestkit Nested cross-validation with calibration, threshold optimization, and statistical tests

Selected Publications

For a complete list, see my Google Scholar profile.

Pinned Loading

  1. combatlearn combatlearn Public

    The ComBat algorithm for a learning framework (scikit-learn compatible)

    Python 6 1

  2. MaldiAMRKit MaldiAMRKit Public

    Comprehensive toolkit for MALDI-TOF mass spectrometry data preprocessing for antimicrobial resistance (AMR) prediction purposes

    Python 3 1

  3. ResPredAI ResPredAI Public

    Implementation of the pipeline described in the work "Artificial intelligence model to predict resistances in Gram-negative bloodstream infections" by Bonazzetti et al., npj Digit. Med. 8, 319 (2025)

    Python 5

  4. phenocluster phenocluster Public

    PhenoCluster: a flexible data-driven framework for identifying clinical phenotypes using latent class and profile analysis

    Python 1

  5. Physics4MedicineLab/CATS Physics4MedicineLab/CATS Public

    Comparing Cas Activities by Target Superimposition (CATS)

    Python 3

  6. nestkit nestkit Public

    nestkit: a nested cross-validation toolkit for scikit-learn

    Jupyter Notebook 1