Author

Augusto Miliorini Amendola

Universidade de São PauloORCID 0000-0002-9407-6270Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 2 Assay:metagenomics· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1

Frequent Co-Authors

Alice de Moura EmilioiD· 2 shared Amanda Gonçalves BendiaiD· 2 shared Célio Roberto JönckiD· 2 shared Daniel Leite MoreiraiD· 2 shared Danilo Cândido VieiraiD· 2 shared Fabiana S. PaulaiD· 2 shared Francielli Vilela PeresiD· 2 shared Frederico Pereira BrandiniiD· 2 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersMicrobiology

Alice de Moura EmilioORCID

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2 papers

Recent·Most cited

MicrobiologyScientific ReportsPeer-reviewedApr 22, 2026

Microbial signatures define the ecosystem functions of the pelagic microbiome in a basin-scale, Southwest Atlantic Ocean

Natascha Menezes Bergo, Francielli Vilela Peres, Danilo Cândido Vieira…·Vivian H. Pellizari

The paper analyzes the pelagic microbiome of the Santos Basin using metagenomics and machine learning to identify microbial signatures and ecosystem functions shaped by local oceanographic processes.

1 citation

metagenomicsmachine learning (classical)

𝕏 PostBluesky

MicrobiologyFEMS MicrobesPeer-reviewedJan 1, 2026

A Machine Learning Approach Elucidates Spatial Patterns of Environmental Properties Driving Microbial Composition Over Santos Basin, South Atlantic

Júlio Cezar Fornazier Moreira, Flúvio Modolon, Natascha Menezes Bergo…·Vivian H. Pellizari

This study uses machine learning to identify microbial assemblages in the Santos Basin, finding that environmental factors like temperature, salinity, and nutrients drive community structure across different depths.

1 citation

metagenomicsmachine learning (classical)

𝕏 PostBluesky