Author

Yasin Kaymaz

Harvard UniversityORCID 0000-0002-9725-7536Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Bioinformatics· 2 Genomics· 2

Signature Assays, Organisms & Methods

Click to filter · or open tag

Assay:scRNA-seq· 3 Approach:statistical model· 3 Research Focus:differential expression· 2 Organism:human· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Ming TangiD· 2 shared Timothy B. SacktoniD· 2 shared Amir Amiri TabatiD· 1 shared Ann Marie MoormanniD· 1 shared Brandon L. LogemaniD· 1 shared Catherine G. DulaciD· 1 shared Cliff I. OduoriD· 1 shared Elyar Bafendeh ZendehiD· 1 shared

Activity by Year

2020-2026 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 13 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

2 shared papersBioinformatics

Ming TangORCID

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

PeakATail: precision-first poly(A)-site calling and calibrated alternative polyadenylation analysis in single-cell RNA-seq

Amir Amiri Tabat, Elyar Bafendeh Zendeh, Yasin Kaymaz

PeakATail identifies poly(A)-sites in single-cell RNA-seq with precision, controlling error rates and validating results against curated data.

bioRxiv · Sep 24, 2026 · PreprintGenomics

Evaluating single-cell cluster stability using the Jaccard similarity index

Ming Tang, Yasin Kaymaz, Brandon L. Logeman, Stephen W. Eichhorn, Zhengzheng Liang, Catherine G. Dulac, Timothy B. Sackton

The paper introduces tools to evaluate single-cell clustering stability using subsampling and the Jaccard similarity index, with an R package and Snakemake workflow for implementation.

Bioinformatics · Nov 2, 2020Peer-reviewedBioinformatics

HieRFIT: Hierarchical Random Forest for Information Transfer

Yasin Kaymaz, Florian Ganglberger, Ming Tang, Francesc Fernández-Albert, Nathan Lawless, Timothy B. Sackton

HieRFIT improves cell type classification accuracy by using hierarchical random forests with prior cell type relationships, showing effectiveness in cross-dataset and cross-species analyses.

bioRxiv · Sep 16, 2020 · PreprintBioinformatics

Epstein Barr virus genomes reveal population structure and type 1 association with endemic Burkitt lymphoma

Yasin Kaymaz, Cliff I. Oduor, Özkan Aydemir, Micah A. Luftig, Juliana A. Otieno, John Michael Ong’echa, Jeffrey A. Bailey, Ann Marie Moormann

The paper analyzes EBV genomes from eBL patients and controls in Kenya, finding type 1 EBV is more common in patients and identifying genetic variants associated with eBL.

Journal of Virology · Jun 19, 2020Peer-reviewedGenomics