Author

Hiranmayi Ranganathan

Lawrence Livermore National Laboratory

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 Microbiology· 2

Signature Assays, Organisms & Methods

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Organism:human· 3 Assay:metagenomics· 3 Research Focus:benchmarking· 2 Approach:deep learning· 2 Research Focus:differential expression· 2 Approach:statistical model· 2 Research Focus:database / resource· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

André Ricardo GonçalvesiD· 3 shared Car Reen KokiD· 3 shared Crystal J. JaingiD· 3 shared James B. ThisseniD· 3 shared Jose Manuel MartíiD· 3 shared Nicholas A. BeiD· 3 shared Nisha J MulakkeniD· 3 shared Boya ZhangiD· 2 shared

Activity by Year

2022-2026 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersMicrobiology

André Ricardo GonçalvesORCID

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4 papers

Recent·Most cited

Meta2DB: Curated Shotgun Metagenomic Feature Sets and Metadata for Health State Prediction

Car Reen Kok, Nisha J Mulakken, James B. Thissen, Jose Manuel Martí, Ryan Lee, Jacob B. Trainer, Andre R Goncalves, Hiranmayi Ranganathan, Aram Avila-Herrera, Crystal J. Jaing, Nicholas A. Be

Meta2DB provides a curated metagenomic database with 13,897 samples across 84 studies for predicting human health states using ML models.

Bioinformatics · Jul 1, 2026Peer-reviewedMicrobiology

An Embeddings Fusion Approach Predicts Disease State from Microbiome Features

Camilo Valdes, André Ricardo Gonçalves, Haonan Zhu, Boya Zhang, Hiranmayi Ranganathan, James B. Thissen, Jose Manuel Martí, Car Reen Kok, Nisha Mulakken, Crystal J. Jaing, Nicholas A. Be

The paper introduces an embeddings fusion approach to predict disease state from microbiome features using visual representations of taxonomic data, achieving high classification accuracy across multiple factors.

bioRxiv · Apr 23, 2026 · PreprintBioinformatics

Beyond Microbial Abundance: Metadata Integration Enhances Disease Prediction in Human Microbiome Studies

André Ricardo Gonçalves, Hiranmayi Ranganathan, Camilo Valdes, Haonan Zhu, Boya Zhang, Car Reen Kok, Jose Manuel Martí, Nisha Mulakken, James B. Thissen, Crystal J. Jaing, Nicholas A. Be

This study integrates host and sample metadata with microbiome abundance profiles to improve disease prediction models using machine learning.

Frontiers in Microbiology · Jan 21, 2026Peer-reviewedMicrobiology

Model Choice Metrics to Optimize Profile-QSAR Performance

Stewart He, Sookyung Kim, Kevin McLoughlin, Hiranmayi Ranganathan, Da Shi, Jonathan Allen

This paper evaluates pQSAR 2.0's performance on GPCR and safety targets, finding it improves models on some assays but not all, with compound assay coverage being a key factor.

bioRxiv · Aug 22, 2022 · PreprintBioinformatics