Author

Arnav Gupta

University of Pennsylvania

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

Biology Disciplines

Click to filter author papers

Bioengineering· 1 Genomics· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:drug response / drug discovery· 2 Organism:human· 2 Assay:clinical cohort / trial· 1 Approach:experimental assay· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1 Organism:mouse· 1

Frequent Co-Authors

Adam H. BiedrzyckiiD· 1 shared Amber LimiD· 1 shared Apoorva S. KelkariD· 1 shared Arthi VaidyanathaniD· 1 shared Barclay B. PowelliD· 1 shared Carmen Le· 1 shared Charlie Longtine· 1 shared Chase Bowman· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

1 shared paperGenomics

Adam H. BiedrzyckiORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

A Multispecies, Modality-Agnostic Scalable In Vivo Mosaic Screening Platform for Therapeutic Target Discovery

Vishwaraj Sontake, Vinay K. Kartha, Neety Sahu, Daniel Fuentes, Linda Chio, Hikaru Miyazaki, Jingshu Chen, Arnav Gupta, Jesi Nonora, Arthi Vaidyanathan, Smitha Shambhu, Gayathri Donepudi, Carmen Le, Lianna Fung, Amber Lim, , Martin Borch Jensen

The paper presents an in vivo screening platform for validating therapeutic targets across species and modalities, identifying functional targets in fibrosis and osteoarthritis models.

bioRxiv · Mar 4, 2026 · PreprintGenomics

Predicting Post-Stroke Aphasia Speech Performance from Multimodal Data with Explainable Machine Learning

Shreya Parchure, Arnav Gupta, Apoorva S. Kelkar, Leslie Vnenchak, Olufunsho K. Faseyitan, John Dominic Medaglia, Denise Y. Harvey, H. Branch Coslett, Roy H. Hamilton

This paper develops a machine learning model to predict post-stroke aphasia speech performance using multimodal data, achieving high accuracy with explainable features.

bioRxiv · Feb 5, 2026 · PreprintBioengineering