Author

Liron Zahavi

Weizmann Institute of ScienceORCID 0000-0001-8867-1287Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Approach:deep learning· 2 Organism:human· 2 Assay:metagenomics· 2 Research Focus:benchmarking· 1 Research Focus:clustering· 1 Approach:contrastive learning· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Eran SegaliD· 2 shared Katherine S. PollardiD· 2 shared Veronika B. DubinkinaiD· 2 shared Adina WeinbergeriD· 1 shared Anastasia GodnevaiD· 1 shared Byron J. SmithiD· 1 shared Chunyu ZhaoiD· 1 shared Jacqueline Moltzau AndersoniD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

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10 top collaborators · 33 mutual co-author links

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2 shared papersSystems Biology

Eran SegalORCID

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2 papers

Recent·Most cited

Systems BiologybioRxivPreprintOct 19, 2025

Self-supervised learning enables robust microbiome predictions in data-limited and cross-cohort settings

Liron Zahavi, Zachary Levine, Anastasia Godneva…·Eran Segal

This paper develops self-supervised learning models for gut microbiome metagenomic data, showing improved prediction performance in data-limited and cross-cohort settings.

DOI 10.1101/2025.10.19.683269

humanmetagenomics

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MicrobiologybioRxivApr 10, 2024

Accurate estimation of intraspecific microbial gene content variation in metagenomic data with MIDAS v3 and StrainPGC

Byron J. Smith, Chunyu Zhao, Veronika B. Dubinkina…·Katherine S. Pollard

The paper introduces StrainPGC and MIDAS v3 to accurately estimate intraspecific microbial gene content variation in metagenomic data, outperforming existing methods and revealing functional diversity in gut microbiomes.

13 citations

humanmetagenomics

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