Author

Pooja Pawar

Lincoln University

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· 3

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Assay:proteomics· 2 Research Focus:database / resource· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Sandhya SamarasingheiD· 3 shared Don KulasiriiD· 1 shared

Activity by Year

2026-2026 · peak 3/yr

Co-Author Collaboration Network

2 top collaborators · 1 mutual co-author link

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3 shared papersBioinformatics

Sandhya SamarasingheORCID

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3 papers

Recent·Most cited

A novel vaccine and drug targets for global eradication of bovine tuberculosis: Holistic frameworks for construction of a potent vaccine and identification of drug targets

Pooja Pawar, Sandhya Samarasinghe, Don Kulasiri

This paper identifies potential vaccine candidates and drug targets for bovine tuberculosis using bioinformatics approaches and proteomic analysis of Mycobacterium bovis strains.

bioRxiv · May 12, 2026 · PreprintBioinformatics

A vaccine for global eradication of TB - A novel conceptual framework and design of a potent peptide-based vaccine with universal coverage through advanced computational vaccinology

Pooja Pawar, Sandhya Samarasinghe

This paper proposes a peptide-based TB vaccine using computational vaccinology to identify conserved epitopes across TB strains, aiming for broad-spectrum protection and universal coverage.

bioRxiv · May 7, 2026 · PreprintBioinformatics

Confronting global eradication of TB head on: Uncovering the root of drug resistance and bacterial survival strategies through a comprehensive computational study of first-line TB drug resistant mutations

Pooja Pawar, Sandhya Samarasinghe

This study identifies global patterns of drug-resistant mutations in Mycobacterium tuberculosis and reveals a strategy where bacteria prefer less harmful mutations that reduce drug binding affinity while maintaining protein stability.

bioRxiv · May 1, 2026 · PreprintBioinformatics