Fabio Cumbo

The paper introduces the Microbiology Galaxy Lab, a community-driven platform for reproducible and FAIR microbial data analysis, offering tools for various omics data types and use cases.

This paper introduces muODE, a framework that simulates microbial community dynamics using metagenomics data to predict FMT outcomes and C. difficile resistance.

GalaxyMemMD provides a reproducible, high-throughput pipeline for membrane protein simulation and analysis, integrating embedding, MD simulation, and downstream analysis.

HyperSketch provides a structure-aware genomic similarity method using de Bruijn graphs and Hyperdimensional Computing, achieving high-resolution strain clustering and deep evolutionary scaling.

This paper uses Hyperdimensional Computing to predict chemical compound toxicity efficiently, showing it can classify compounds as toxic or non-toxic with computational advantages over traditional methods.

The paper introduces MetaSBT, a tool for classifying microbial genomes and MAGs, and presents databases of over 190,000 viral genomes, including many previously unknown species clusters.

The paper introduces Meta16S, a scalable pipeline for incremental clustering of 16S rRNA data to discover and classify unknown microbes.

The paper introduces a Vector-Symbolic Architecture for encoding viral pangenomes and evaluates its effectiveness in species classification, finding that a flat species-level model outperforms genus-level and hierarchical approaches.

The paper identifies M1 muscarinic and H3 histamine receptors as targets for OPC differentiation and analyzes their ligand interactions using computational methods.

R2G2 automates the creation of Galaxy wrappers for R/Bioconductor packages, simplifying integration and reducing complexity for non-programmers.

This paper compares classical machine learning and a brain-inspired hyperdimensional computing approach for classifying metagenomic samples, showing HDC achieves comparable or better accuracy and efficiency.

This study uses the Gini Index to compare gene expression specialization between normal and tumor cells, finding that tumor cells show reduced specialization.

The paper introduces a novel backward variable selection method for metagenomic data to identify microbial biomarkers in colorectal cancer, showing promising results compared to existing methods.

Marker genes for 26,970 species-level genome bins, 4,992 defined only by metagenome-assembled genomes, let MetaPhlAn 4 quantify uncharacterized species and beat MetaPhlAn 3, mOTUs and Bracken on synthetic benchmarks.

Bioconda provides a sustainable, comprehensive distribution of bioinformatics software using Conda for reproducible analysis across platforms.