Author

Rani Shlayem

Tel Aviv University

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 Genomics· 2

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Assay:bulk RNA-seq· 2 Research Focus:differential expression· 2 Organism:human· 2 Assay:whole-genome sequencing· 2 Approach:Bayesian inference· 1 Research Focus:benchmarking· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Hadas VolkoviD· 4 shared Noam ShomroniD· 4 shared Meitar GradiD· 3 shared Maria Raitses‐GurevichiD· 2 shared Tami RubinekiD· 2 shared Amit LevoniD· 1 shared Artem DanilevskyiD· 1 shared David GurwitziD· 1 shared

Activity by Year

2026-2026 · peak 4/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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4 shared papersGenomics

Hadas VolkovORCID

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

RNA-sequencing of butyrate-treated human lymphoblastoid cell lines shows dysregulated genes of multiple disease-related pathways

Hadas Volkov, Tehila Cohen, Rani Shlayem, Elad Shapira, Irena Voinsky, Hana Klin, Meitar Grad, Noam Shomron, David Gurwitz

This study uses RNA-seq to analyze the transcriptomic effects of butyrate on human lymphoblastoid cell lines, identifying dysregulated genes in multiple disease-related pathways.

bioRxiv · Oct 24, 2026 · PreprintGenomics

Comparative analysis of Illumina and Ultima-Genomics sequencing for plasma cell-free small RNA profiling in pancreatic cancer

Amit Levon, Hadas Volkov, Rani Shlayem, Noam Shomron

This study compares Illumina and Ultima-Genomics platforms for plasma cell-free small RNA profiling in pancreatic cancer, finding both produce biologically relevant results with platform-specific differences.

Scientific Reports · Sep 30, 2026Peer-reviewedGenomics

Simulation-based Bayesian deep learning enables uncertainty-aware tumor fraction estimation in cell-free DNA

Hadas Volkov, Maria Raitses‐Gurevich, Meitar Grad, Rani Shlayem, Artem Danilevsky, Tami Rubinek, Malka Gorfine, Noam Shomron

This paper introduces purNPE, a Bayesian deep learning framework for estimating tumor fraction in cell-free DNA without labeled cancer samples, achieving comparable performance to existing methods.

BioData Mining · Aug 6, 2026Peer-reviewedBioinformatics

Exploring per-base quality scores as a surrogate marker of cell-free DNA fragmentome

Hadas Volkov, Maria Raitses‐Gurevich, Meitar Grad, Rani Shlayem, Dev Leibowitz, Tami Rubinek, Talia Golan, Noam Shomron

The paper shows that per-base quality scores in cfDNA whole-genome sequencing can classify cancer samples from controls with high accuracy using unsupervised analysis.

bioRxiv · May 5, 2026 · PreprintBioinformatics