Author

Sasa Milosavljevic

Nofer Institute of Occupational MedicineORCID 0000-0002-4757-4111Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genomics· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Organism:human· 2 Approach:machine learning (classical)· 2 Approach:statistical model· 2 Research Focus:variant calling· 2 Assay:whole-genome sequencing· 2 Assay:bulk RNA-seq· 1

Frequent Co-Authors

David Georgievich ZaridzeiD· 3 shared Ivana HolcátováiD· 3 shared Paul BrennaniD· 3 shared Valérie GaborieauiD· 3 shared Angela Cecilia PesatoriiD· 2 shared Anush Mukeria· 2 shared Azhar KhandekariD· 2 shared Beata ŚwiątkowskaiD· 2 shared

Activity by Year

2020-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersGenomics

David Georgievich ZaridzeORCID

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3 papers

Recent·Most cited

A prognostic signature for lung adenocarcinoma in people who have never smoked

Wei Zhao, Tongwu Zhang, Xing Hua, Phuc H. Hoang, Mona Miraftab, Monjoy Saha, John P. McElderry, Jian Sang, Olivia W. Lee, Caleb Hartman, Azhar Khandekar, Sunandini Sharma, Frank J. Colón-Matos, Samuel Anyaso-Samuel, Difei Wang, , Maria Teresa Landi

The paper identifies three subtypes of NS-LUAD using RNA-seq data and develops a 60-gene signature that predicts survival across subgroups.

bioRxiv · Aug 13, 2025 · PreprintGenomics

Deciphering lung adenocarcinoma evolution and the role of LINE-1 retrotransposition

Tongwu Zhang, Wei Zhao, Christopher Wirth, Marcos Díaz‐Gay, Jinhu Yin, Monia Cecati, Francesca Marchegiani, Phuc H. Hoang, Charles Leduc, Marina K. Baine, William David Travis, Lynette Marie Sholl, Philippe Joubert, Jian Sang, John P. McElderry, , Maria Teresa Landi

This study analyzes 1024 lung adenocarcinomas to identify evolutionary patterns and mutational mechanisms, revealing distinct trajectories linked to smoking, ancestry, and retrotransposition.

bioRxiv · Mar 14, 2025 · PreprintGenomics

Needlestack: an ultra-sensitive variant caller for multi-sample next generation sequencing data

Tiffany Myriam Delhomme, Patrice Hodonou Avogbe, Aurélie AG Gabriel, Nicolas Alcala, Noémie Leblay, Catherine Voegele, Maxime Vallée, Priscilia Chopard, Amélie Chabrier, Behnoush Abedi‐Ardekani, Valérie Gaborieau, Ivana Holcátová, Vladimír Janout, Lenka Foretová, Sasa Milosavljevic, , Matthieu Foll

Needlestack is a variant caller that improves sensitivity for low abundance mutations by learning sequencing error rates from multi-sample data.

NAR Genomics and Bioinformatics · Apr 20, 2020Peer-reviewedBioinformatics