Author

Y. Louzoun

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

Click to filter author papers

Immunology· 5 Microbiology· 3

Signature Assays, Organisms & Methods

Click to filter · or open tag

Organism:human· 5 Approach:deep learning· 4 Approach:statistical model· 4 Assay:bulk RNA-seq· 2 Research Focus:cell type annotation· 2 Assay:CITE-seq· 2 Research Focus:clustering· 2 Research Focus:differential expression· 2

Frequent Co-Authors

M. Ben Izhak· 3 shared O. Koren· 3 shared S. Dvorkin· 3 shared A. belogolovski· 2 shared N. Tickotsky-Moskovitz· 2 shared R. Levi· 2 shared A. A. Kuperman· 1 shared A. alis· 1 shared

Activity by Year

2018-2022 · peak 5/yr

Co-Author Collaboration Network

10 top collaborators · 12 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

3 shared papersMicrobiology

M. Ben Izhak

Mutual co-authors in cluster

Author profile →

8 papers

Recent·Most cited

Counting Is Almost All You Need

O. Akerman, Y. Louzoun, H. Isakov, R. Levi

This paper shows that counting T-cell receptor repertoires outperforms existing methods in detecting CMV and HLA alleles, with an attention model and a simplified graph approach improving accuracy.

bioRxiv · Aug 9, 2022Immunology

Progesterone supplementation in mice leads to microbiome alterations and weight gain in a sex-specific manner

M. Nuriel-Ohayon, A. belogolovski, S. Komissarov, M. Ben Izhak, O. Shtossel, H. Neuman, O. Ziv, S. Turjeman, S. Bel, Y. Louzoun, O. Koren

This study shows progesterone increases weight in female mice but not males, with microbiome changes in females only, suggesting sex-specific microbiota involvement in progesterone effects.

bioRxiv · Oct 6, 2021 · PreprintMicrobiology

MIPMLP - Microbiome Pre-processing Machine Learning Pipeline.

Y. Jasner, A. belogolovski, M. Ben Izhak, O. Koren, Y. Louzoun

The paper develops a preprocessing pipeline for 16S sequencing data to improve machine learning classification accuracy by optimizing feature representation and dimensionality reduction.

bioRxiv · Nov 24, 2020 · PreprintMicrobiology

Projection of gut microbiome pre and post-bariatric surgery to predict surgery outcome

M. Ben Izhak, A. Eshel, r. cohen, L. Madar-Shapiro, h. meiri, C. Wachtel, C. Leung, E. Messick, N. Jongkam, E. mavor, S. Sapozhnikov, N. Maharashak, S. Abu-Abeid, A. alis, I. Mahler, , Y. Louzoun

The paper projects gut microbiome data pre and post-bariatric surgery to predict BMI loss, identifying distinct microbiome profiles associated with different outcomes.

bioRxiv · Aug 27, 2020Microbiology

Autoencoder based local T cell repertoire density can be used to classify samples and T cell receptors

S. Dvorkin, R. Levi, Y. Louzoun

The paper introduces ELATE, an autoencoder-based method to estimate local T cell repertoire density, enabling classification of samples and prediction of TCR-peptide binding.

bioRxiv · Jun 12, 2020Immunology

CDR3 and V- genes show distinct reconstitution patterns in T-cell repertoire post allogenic bone marrow transplantation

N. Tickotsky-Moskovitz, Y. Louzoun, S. Dvorkin, A. Rotkopf, A. A. Kuperman, S. Efroni

The paper tracks T-cell repertoire changes post allogeneic bone marrow transplantation, finding that CDR3 sequences remain donor-derived while V-gene usage shifts toward the recipient over time.

bioRxiv · Mar 5, 2020Immunology

Prediction of specific TCR-peptide binding from large dictionaries of TCR-peptide pairs

I. Springer, H. Besser, N. Tickotsky-Moskovitz, S. Dvorkin, Y. Louzoun

The paper introduces ERGO, a TCR-peptide binding predictor using NLP and deep learning, which outperforms existing methods in multiple benchmark tests.

bioRxiv · Jan 19, 2020Immunology

Single haplotype admixture models using large scale HLA genotype frequencies to reproduce human admixture

A. L. Simanovsky, A. Madbouly, M. Halagan, M. Maiers, Y. Louzoun

This paper develops an HLA-based method to resolve population admixture using haplotype frequencies, identifying 8 ethnic groups that explain 90% of variance in 56 populations.

bioRxiv · Jun 1, 2018Immunology