Author

Mohammed El Anbari

National Institutes of Health

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· 2 Genetics· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Assay:protein sequence· 2 Approach:statistical model· 2 Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Reda RawiiD· 2 shared Tatsiana BylundiD· 2 shared Tongqing ZhouiD· 2 shared Alexandra Katharina MarriD· 1 shared Andrew J. SchaubiD· 1 shared Aouatef Ismail ChouchaneiD· 1 shared Emily Tourtellott‐Fogt· 1 shared Krisha McKee· 1 shared

Activity by Year

2020-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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2 shared papersBioinformatics

Reda RawiORCID

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Author profile →

3 papers

Recent·Most cited

VIProDesign: Viral Protein Panel Design for Highly Variable Viruses to Evaluate Immune Responses and Identify Broadly Neutralizing Antibodies

Tatsiana Bylund, Mohammed El Anbari, Andrew J. Schaub, Tongqing Zhou, Reda Rawi

VIProDesign is a computational tool that designs viral protein panels to balance diversity and size, enabling better evaluation of immune responses and identification of broadly neutralizing antibodies.

BMC Bioinformatics · Aug 21, 2026Peer-reviewedBioinformatics

BRAVE: a highly accurate method for predicting HIV-1 antibody resistance using large language models for proteins

Mohammed El Anbari, Tatsiana Bylund, Sijy J. O'Dell, Emily Tourtellott‐Fogt, Krisha McKee, Stephen D. Schmidt, Nonhlanhla N. Mkhize, Penny L. Moore, Nicole A. Doria‐Rose, Tongqing Zhou, Reda Rawi

BRAVE predicts HIV-1 resistance to 33 bNAbs using a Random Forests classifier with a protein language model, outperforming existing tools in accuracy and other metrics.

bioRxiv · Jul 28, 2025 · PreprintBioinformatics

In silico analysis of imprinted gene expression in the mouse skin.

Alexandra Katharina Marr, Sabri Boughorbel, Mohammed Toufiq, Mohammed El Anbari, Aouatef Ismail Chouchane, Tomoshige Kino

The paper analyzes imprinted gene expression in mouse skin, finding elevated expression of 16 IGN genes during telogen and early anagen phases and suggesting they regulate hair cycle-associated genes.

bioRxiv · Jun 4, 2020 · PreprintGenetics