Author

Ewa Kizling

University of Warsaw

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Organism:human· 2 Assay:bulk RNA-seq· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Approach:statistical model· 1

Frequent Co-Authors

Eike StaubiD· 2 shared Ewa SzczurekiD· 2 shared Krzysztof Koras· 2 shared Anna M. L. Coenen-StassiD· 1 shared Dilafruz JuraevaiD· 1 shared Krzysztof KolmusiD· 1 shared Magda MarkowskaiD· 1 shared Magdalena A. BudzinskaiD· 1 shared

Activity by Year

2021-2022 · peak 1/yr

Co-Author Collaboration Network

9 top collaborators · 31 mutual co-author links

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2 shared papersBioinformatics

Eike StaubORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

SLIDE-VIP: a comprehensive, cell line- and patient-based framework for synthetic lethality prediction in DNA damage repair, chromatin remodeling and cell cycle

Magda Markowska, Magdalena A. Budzinska, Anna M. L. Coenen-Stass, Senbai Kang, Ewa Kizling, Krzysztof Kolmus, Krzysztof Koras, Eike Staub, Ewa Szczurek

SLIDE-VIP identifies synthetic lethal gene pairs in DNA damage repair, chromatin remodeling, and cell cycle using multi-omics data and eight statistical tests, reducing candidate pairs by 250-fold.

bioRxiv · Jul 7, 2022 · PreprintBioinformatics

Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines

Krzysztof Koras, Ewa Kizling, Dilafruz Juraeva, Eike Staub, Ewa Szczurek

The paper develops a deep learning model called DEERS to predict kinase inhibitor efficacy in cancer cell lines, achieving high accuracy and providing interpretable insights into drug response mechanisms.

Scientific Reports · Aug 6, 2021Peer-reviewedBioinformatics