Author

Nazmul Haque

Shahjalal University of Science and TechnologyORCID 0009-0009-3145-3065Unclaimed ORCID profile · follow to get notified of new preprints

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Nazmul Haque1 paper · Tulane University

Biology Disciplines

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Bioinformatics· 1

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Research Focus:benchmarking· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1 Research Focus:variant calling· 1 Assay:whole-genome sequencing· 1

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Abdul MazediD· 1 shared Jannatul Nayeem Ankhi· 1 shared Md Jamal UddiniD· 1 shared

Activity by Year

2026-2026 · peak 1/yr

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3 top collaborators · 3 mutual co-author links

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1 shared paperBioinformatics

Abdul MazedORCID

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Recent·Most cited

BioinformaticsbioRxivPreprintApr 2, 2026

TF-IDF k-mer-based Classical and Hybrid Machine Learning Models for SARS-CoV-2 Variant Classification under Imbalanced Genomic Data

Nazmul Haque, Abdul Mazed, Jannatul Nayeem Ankhi, Md Jamal Uddin

This study compares machine learning models for SARS-CoV-2 variant classification under imbalanced data, finding classical models with TF-IDF k-mer features outperform deep learning methods.

DOI 10.64898/2026.04.02.716024

whole-genome sequencingmachine learning (classical)

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