Author

Zeba Sanjida

National Institute of Biotechnology

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· 1 Microbiology· 1

Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 1 Approach:machine learning (classical)· 1 Assay:proteomics· 1 Approach:statistical model· 1 Research Focus:variant calling· 1 Organism:virus· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Arittra BhattacharjeeiD· 2 shared Asma RahmaniD· 2 shared Ishtiaque AhammadiD· 2 shared M. SalimullahiD· 2 shared Marjia Akter Suchi· 2 shared Mohammad Nazmus SakibiD· 2 shared SM Sajid Hasan· 2 shared Zeshan Mahmud ChowdhuryiD· 2 shared

Activity by Year

2025-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersMicrobiology

Arittra BhattacharjeeORCID

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2 papers

Recent·Most cited

Linking Genomic Landscape to Disease Mechanism: Core Genetic Factors Underlying Pathogenesis and Antimicrobial Resistance in Diarrheal Pathogens

Mohammad Uzzal Hossain, Marjia Akter Suchi, Zeba Sanjida, Asma Rahman, Mohammad Nazmus Sakib, SM Sajid Hasan, Md. Shorif Hossan, Abu Nayeem Ahmed, Mahmudul Hasan, Tamal Paul, Arittra Bhattacharjee, Zeshan Mahmud Chowdhury, Ishtiaque Ahammad, Md Musharaf Hossain, Palash Kumar Sarker, , Keshob Chandra Das

This study uses whole-genome sequencing and pan-genome analysis of Bangladeshi diarrheal pathogens to identify core genes involved in pathogenesis and antibiotic resistance.

bioRxiv · Aug 31, 2025 · PreprintMicrobiology

APDeeM: A machine Learning strategy towards Effective Peptide Vaccine Candidates Identification against Different Types of Viruses

Mohammad Uzzal Hossain, Md. Romzan Alom, SM Sajid Hasan, Mohammad Nazmus Sakib, Marjia Akter Suchi, Zeba Sanjida, Asma Rahman, Arittra Bhattacharjee, Zeshan Mahmud Chowdhury, Ishtiaque Ahammad, Muhammad Aminur Rahman, Saiful Azad, M. Salimullah

The paper introduces APDeeM, an ensemble machine learning system for identifying antiviral peptide candidates, achieving high accuracy and efficiency compared to existing methods.

bioRxiv · Aug 25, 2025 · PreprintBioinformatics