Author

Venkata Suhas Maringanti

University of Massachusetts Dartmouth

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

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Research Focus:benchmarking· 2 Organism:human· 2 Assay:metagenomics· 2 Approach:Bayesian inference· 1 Approach:deep learning· 1 Approach:machine learning (classical)· 1

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Georg Kurt GerberiD· 2 shared Vanni BucciiD· 2 shared

Activity by Year

2020-2022 · peak 1/yr

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2 shared papersBioinformatics

Georg Kurt GerberORCID

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2 papers

Recent·Most cited

BioinformaticsmSystemsPeer-reviewedSep 7, 2022

MDITRE: scalable and interpretable machine learning for predicting host status from temporal microbiome dynamics

Venkata Suhas Maringanti, Vanni Bucci, Georg Kurt Gerber

MDITRE predicts host status from microbiome time-series data using interpretable deep learning, outperforming existing methods in speed and accuracy.

18 citations

humanmetagenomics

𝕏 PostBluesky

BioinformaticsbioRxivPreprintJun 25, 2020

Scalable learning of interpretable rules for the dynamic microbiome domain

Venkata Suhas Maringanti, Vanni Bucci, Georg Kurt Gerber

The paper introduces a scalable, differentiable model for learning interpretable rules from microbiome time-series data to classify host status, achieving comparable performance to a Bayesian method with faster runtime and more rules.

DOI 10.1101/2020.06.25.172270

humanmetagenomics

𝕏 PostBluesky