Author

Hana Susak

German Cancer Research CenterORCID 0000-0002-7965-7675Unclaimed ORCID profile · follow to get notified of new preprints

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Approach:Bayesian inference· 2 Organism:human· 2 Research Focus:variant effect prediction· 2 Assay:whole-exome sequencing· 2 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Stephan OssowskiiD· 2 shared Xavier EstivilliD· 2 shared Francesc MuyasiD· 1 shared Geòrgia EscaramísiD· 1 shared German M. DemidoviD· 1 shared Laura DomènechiD· 1 shared Laura SerraiD· 1 shared Luis ZapataiD· 1 shared

Activity by Year

2017-2021 · peak 1/yr

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10 top collaborators · 33 mutual co-author links

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2 shared papersBioinformatics

Stephan OssowskiORCID

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2 papers

Recent·Most cited

Efficient and Flexible Integration of Variant Characteristics in Rare Variant Association Studies Using Integrated Nested Laplace Approximation

Hana Sušak, Laura Serra, German M. Demidov, Raquel Rabionet, Laura Domènech, Mattia Bosio, Francesc Muyas, Xavier Estivill, Geòrgia Escaramís, Stephan Ossowski

The paper introduces BATI, a Bayesian rare variant association test that outperforms existing methods in detecting rare variants associated with complex diseases, especially when genetic variation explains little phenotypic variance.

PLoS Computational Biology · Feb 19, 2021Peer-reviewedBioinformatics

Bayesian Inference Of Cancer Driver Genes Using Signatures Of Positive Selection

Luis Zapata, Hana Susak, Oliver Drechsel, Marc R. Friedländer, Xavier Estivill, Stephan Ossowski

This study introduces a Bayesian model to identify cancer driver genes by integrating evolutionary signatures, revealing novel gene-tumor type associations and highlighting chromatin modifiers as key targets.

bioRxiv · Apr 13, 2017 · PreprintCancer Biology