Author

Shreeya Mhade

Indiana University BloomingtonORCID 0000-0002-3050-802XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Ecology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Organism:Arabidopsis· 1 Research Focus:benchmarking· 1 Research Focus:ecology & biodiversity· 1 Approach:machine learning (classical)· 1 Assay:protein structure· 1 Research Focus:protein structure prediction· 1

Frequent Co-Authors

Allisandra G. KummeriD· 1 shared André Barretto Bruno WilkeiD· 1 shared Chalmers Vasquez· 1 shared Gandhar TendulkariD· 1 shared Jagadeesh ChitturiiD· 1 shared John Townsend· 1 shared John-Paul MutebiiD· 1 shared Karishma Surendra KaushikiD· 1 shared

Activity by Year

2021-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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1 shared paperEcology

Allisandra G. KummerORCID

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2 papers

Recent·Most cited

Short-term forecasts of Aedes aegypti relative abundance to enhance mosquito control situational awareness

Utkarsh Bhosekar, Paulo Cesar Ventura, Megan D. Hill, Allisandra G. Kummer, Shreeya Mhade, Jagadeesh Chitturi, Chalmers Vasquez, John-Paul Mutebi, John Townsend, Maria Litvinova, André Barretto Bruno Wilke, Marco Ajelli

This paper develops a multi-model forecasting framework to predict Aedes aegypti abundance up to 4 weeks ahead, showing improved performance with longer horizons and highlighting challenges in low activity or atypical peak periods.

bioRxiv · Jul 7, 2026 · PreprintEcology

AMPing up the search: An in silico approach to identifying Antimicrobial Peptides (AMPs) with potential anti-biofilm activity

Shreeya Mhade, Stutee Panse, Gandhar Tendulkar, Rohit Awate, Snehal Kadam, Karishma Surendra Kaushik

The paper presents an in silico method to identify antimicrobial peptides with potential anti-biofilm activity using homology modeling and molecular docking.

bioRxiv · Aug 16, 2021 · PreprintBioinformatics