Author

Gowtham Atluri

University of CincinnatiORCID 0000-0001-5619-6688Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Assay:protein structure· 1

Frequent Co-Authors

Azamat SalamatoviD· 1 shared Chaowen GuaniD· 1 shared Daniel Schnell· 1 shared Jun BaiiD· 1 shared Kashish ChetaliD· 1 shared Meenakshi VenkatasubramanianiD· 1 shared Nathan SalomonisiD· 1 shared

Activity by Year

2020-2025 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 9 mutual co-author links

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1 shared paperBioinformatics

Azamat SalamatovORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintNov 20, 2025

Quantum and Classical Graph Convolutional Neural Networks for Protein Ligand Dissociation Constant Prediction

Azamat Salamatov, Jun Bai, Gowtham Atluri, Chaowen Guan

The paper introduces a hybrid quantum-classical graph neural network to predict protein-ligand dissociation constants, showing improved accuracy and parameter efficiency.

DOI 10.1101/2025.11.20.689635

protein structuredeep learning

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BioinformaticsBioinformaticsPeer-reviewedMar 19, 2020

Resolving single-cell heterogeneity from hundreds of thousands of cells through sequential hybrid clustering and NMF

Meenakshi Venkatasubramanian, Kashish Chetal, Daniel Schnell…·Nathan Salomonis

The paper introduces an improved clustering algorithm for scRNA-seq data that outperforms existing methods on benchmark datasets and scales to large-scale data.

66 citations

humanscRNA-seq

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