Author

Yaron Gurovich

ORCID 0000-0002-9474-3846Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 3 Research Focus:cell type annotation· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Assay:imaging· 1

Frequent Co-Authors

Jean Tori PanteliD· 3 shared Max ZhaoiD· 3 shared Nicole FleischeriD· 3 shared Nurulhuda HajjiriD· 3 shared Peter Michael KrawitziD· 3 shared Stefan MundlosiD· 3 shared Tzung‐Chien HsiehiD· 3 shared Alexej KnausiD· 2 shared

Activity by Year

2018-2019 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersGenetics

Jean Tori PantelORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

PEDIA: Prioritization of Exome Data by Image Analysis

Tzung‐Chien Hsieh, Martin A. Mensah, Jean Tori Pantel, Dione Aguilar, Omri Bar, Allan Bayat, Luis Eduardo Becerra-Solano, Heidi Beate Bentzen, Saskia Biskup, Oleg Borisov, Øivind Braaten, Claudia Ciaccio, Marie Coutelier, Kirsten Cremer, Magdalena Danyel, , Peter Michael Krawitz

The paper introduces an AI-driven method that uses facial images to prioritize exome data, achieving 99% top-10 accuracy in identifying disease-causing genes.

Genetics in Medicine · Jun 4, 2019Peer-reviewedBioinformatics

Advances in computer-assisted syndrome recognition and differentiation in a set of metabolic disorders

Jean Tori Pantel, Max Zhao, Martin A. Mensah, Nurulhuda Hajjir, Tzung‐Chien Hsieh, Yair Hanani, Nicole Fleischer, Tom Kamphans, Stefan Mundlos, Yaron Gurovich, Peter Michael Krawitz

This paper evaluates deep learning for syndrome recognition in metabolic disorders, finding improved accuracy with larger training sets but no significant effect from ethnicity or sex.

Journal of Inherited Metabolic Disease · Apr 5, 2018Peer-reviewedGenetics

Characterization of Glycosylphosphatidylinositol Biosynthesis Defects by Clinical Features, Flow Cytometry, and Automated Image Analysis

Alexej Knaus, Jean Tori Pantel, Manuela Pendziwiat, Nurulhuda Hajjir, Max Zhao, Tzung‐Chien Hsieh, Max Schubach, Yaron Gurovich, Nicole Fleischer, Marten Jäger, Sebastian Köhler, Hiltrud Muhle, Christian M. Korff, Rikke Steensbjerre Møller, Allan Bayat, , Peter Michael Krawitz

The paper evaluates clinical features, flow cytometry, and facial image analysis to classify GPIBDs and identify gene-specific patterns, finding that facial recognition software is most effective for gene prediction.

Genome Medicine · Jan 5, 2018Peer-reviewedGenetics