Author

T. Pupko

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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Evolutionary Biology· 2 Microbiology· 2 Ecology· 1 Immunology· 1

Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 3 Approach:statistical model· 3 Approach:deep learning· 2 Research Focus:differential expression· 2 Assay:whole-genome sequencing· 2 Organism:bacteria (other)· 1 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1

Frequent Co-Authors

O. Avram· 3 shared G. Loewenthal· 2 shared N. Ecker· 2 shared N. Wagner· 2 shared B. A. Vinatzer· 1 shared D. Ben-Meir· 1 shared D. Biran· 1 shared D. Shkedy· 1 shared

Activity by Year

2019-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

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3 shared papersEvolutionary Biology

O. Avram

Mutual co-authors in cluster

Author profile →

6 papers

Recent·Most cited

Evaluation of the Ability of AlphaFold to Predict the Three-Dimensional Structures of Antibodies and Epitopes

K. Polonsky, T. Pupko, N. Freund

This study evaluates AlphaFold's ability to predict antibody structures and interactions, finding limitations in CDR regions and antigen binding predictions.

bioRxiv · Aug 3, 2023Immunology

Genetic and functional diversity help explain pathogenic, weakly pathogenic, and commensal lifestyles in the genus Xanthomonas

M. M. Pena, R. Bhandari, R. M. Bowers, K. Weis, E. Newberry, N. Wagner, T. Pupko, J. B. Jones, T. Woyke, B. A. Vinatzer, M.-A. Jacques, N. Potnis

This study examines the genetic and functional diversity of Xanthomonas strains to understand their pathogenic, weakly pathogenic, and commensal lifestyles through genomic analysis and phylogenetic relationships.

bioRxiv · May 31, 2023 · PreprintEcology

Harnessing machine translation methods for sequence alignment

E. Dotan, Y. Belinkov, O. Avram, E. Wygoda, N. Ecker, M. Alburquerque, O. Keren, G. Loewenthal, T. Pupko

This paper introduces BetaAlign, a sequence alignment method using NLP techniques, which outperforms existing tools like MAFFT and MUSCLE.

bioRxiv · Jul 22, 2022 · PreprintEvolutionary Biology

Harnessing machine learning to unravel protein degradation in Escherichia coli

T. Pupko, N. Nagar, N. Ecker, G. Loewenthal, O. Avram, D. Ben-Meir, D. Biran, E. Ron

The paper uses a combination of experimental and machine learning methods to identify fast-degrading proteins in E. coli and explore factors influencing protein half-lives.

bioRxiv · Oct 4, 2020Microbiology

ModelTeller: model selection for optimal phylogenetic reconstruction using machine learning

S. Abadi, O. Avram, S. Rosset, T. Pupko, I. Mayrose

ModelTeller uses machine learning to select phylogenetic models for accurate branch-length estimation, outperforming traditional statistical methods on complex datasets.

bioRxiv · Jan 9, 2020Evolutionary Biology

Show me your secret(ed) weapons: a multifaceted approach reveals novel type III-secreted effectors of a plant pathogenic bacterium

I. Jimenez guerrero, F. Perez-Montano, G. M. da Silva, N. Wagner, D. Shkedy, M. Zhao, L. Pizarro, M. Bar, R. Walcott, G. Sessa, T. Pupko, S. Burdman

This study identifies 51 T3E genes and seven novel T3Es in Acidovorax citrulli using bioinformatics and experimental validation, revealing its extensive T3E arsenal.

bioRxiv · Jun 22, 2019Microbiology