Author

Jakob Hertzberg

Max Planck Institute for Molecular GeneticsORCID 0000-0002-0581-7939Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Genetics· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Approach:machine learning (classical)· 2 Research Focus:variant calling· 2 Assay:whole-exome sequencing· 2 Approach:statistical model· 1 Research Focus:variant effect prediction· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Stefan MundlosiD· 3 shared Martin VingroniD· 2 shared Alain VerloèsiD· 1 shared Alexej KnausiD· 1 shared Alfredo OrricoiD· 1 shared Allan BayatiD· 1 shared Angela Maria KaindliD· 1 shared Anna Keryan· 1 shared

Activity by Year

2019-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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3 shared papersBioinformatics

Stefan MundlosORCID

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3 papers

Recent·Most cited

Accurate detection of pathogenic structural variants guided by multi-platform comparison

Nico Alavi, M-Hossein Moeinzadeh, Jakob Hertzberg, Uirá Souto Melo, Lion Ward Al Raei, Paolo Infantino, Maryam Ghareghani, Marco Savarese, Stefan Mundlos, Martin Vingron

The paper introduces dicast, a machine-learning method that improves the detection of pathogenic structural variants from short-read sequencing by leveraging genomic context and alignment features.

bioRxiv · May 21, 2025 · PreprintBioinformatics

TADA - a Machine Learning Tool for Functional Annotation based Prioritisation of Putative Pathogenic CNVs

Jakob Hertzberg, Stefan Mundlos, Martin Vingron, G. Gallone

TADA prioritizes pathogenic CNVs using functional annotations and machine learning, achieving high AUC scores for deletions and duplications.

Genome biology · Mar 1, 2022Peer-reviewedGenetics

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