Author

Abdur Rafi

Bangladesh University of Engineering and TechnologyORCID 0000-0002-3899-0025Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Evolutionary Biology· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:phylogenetics / evolution· 2 Approach:statistical model· 2 Organism:multiple species· 1

Frequent Co-Authors

Ahmed Mahir Sultan RumiiD· 2 shared Md. Shamsuzzoha BayzidiD· 2 shared Sheikh Azizul HakimiD· 2 shared Md Toki TahmidiD· 1 shared Rabib Jahin Ibn MominiD· 1 shared Rezwana ReaziD· 1 shared S. SohaibiD· 1 shared Tanjeem Azwad ZamaniD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

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8 top collaborators · 28 mutual co-author links

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2 shared papersEvolutionary Biology

Ahmed Mahir Sultan RumiORCID

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2 papers

Recent·Most cited

Evolutionary BiologybioRxivPreprintApr 4, 2025

Highly Accurate Species Tree Inference from Orthologs and Paralogs through Scalable Quartet Amalgamation

Abdur Rafi, Ahmed Mahir Sultan Rumi, Sheikh Azizul Hakim, Md. Shamsuzzoha Bayzid

This paper introduces wQFM-GDL, a method for species tree inference that models gene duplication and loss, outperforming existing methods on large datasets.

DOI 10.1101/2025.04.04.647228

deep learningstatistical model

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Evolutionary BiologyBioinformatics AdvancesPeer-reviewedDec 26, 2024

wQFM-TREE: highly accurate and scalable quartet-based species tree inference from gene trees

Abdur Rafi, Ahmed Mahir Sultan Rumi, Sheikh Azizul Hakim…·Md. Shamsuzzoha Bayzid

This paper introduces wQFM-TREE, a method for species tree inference that improves accuracy and scalability over existing methods like ASTRAL and wQFM.

3 citations

multiple speciesdeep learning

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