Author

Matteo Eustachio Peluso

SIB Swiss Institute of Bioinformatics

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 2 Systems Biology· 1

Signature Assays, Organisms & Methods

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Assay:metagenomics· 3 Research Focus:benchmarking· 2 Research Focus:database / resource· 1 Approach:deep learning· 1 Approach:large language model· 1

Frequent Co-Authors

Christian von MeringiD· 3 shared Janko TackmanniD· 3 shared Daniela GaioiD· 2 shared David PatschiD· 2 shared Eugenio Perez‐Molphe‐MontoyaiD· 2 shared Lukas MalfertheineriD· 2 shared Nicolas C NäpfliniD· 2 shared Gregor RotiD· 1 shared

Activity by Year

2026-2026 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersBioinformatics

Christian von MeringORCID

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Author profile →

3 papers

Recent·Most cited

Susagi: A Microbiome World Model

Matteo Eustachio Peluso, Janko Tackmann, Christian von Mering

Susagi is a permutation-invariant denoising transformer that models microbial community stability from bacterial SSU rRNA gene embeddings, showing reliable predictions and capturing biological structure.

bioRxiv · May 11, 2026 · PreprintSystems Biology

The MicrobeAtlas database: Global trends and insights into Earth's microbial ecosystems

João F. Matias Rodrigues, Janko Tackmann, Lukas Malfertheiner, David Patsch, Eugenio Perez‐Molphe‐Montoya, Nicolas C Näpflin, Daniela Gaio, Gregor Rot, Mihai Danaila, Matteo Eustachio Peluso, Marija Dmitrijeva, Thomas Schmidt, Christian von Mering

MicrobeAtlas unifies over two million microbiome samples to enable cross-technology insights into microbial ecosystems and their global patterns.

Cell · Feb 25, 2026Peer-reviewedBioinformatics

Enhanced semantic classification of microbiome sample origins using Large Language Models (LLMs)

Daniela Gaio, Janko Tackmann, Eugenio Perez‐Molphe‐Montoya, Nicolas C Näpflin, David Patsch, Lukas Malfertheiner, Matteo Eustachio Peluso, Christian von Mering

This paper uses large language models to automate the annotation of microbiome sequencing records, achieving better performance than a keyword-based approach.

GigaScience · Jan 1, 2026Peer-reviewedBioinformatics