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Publications

Each day our team contributes to the world of discovery and science. It is a vast database of expertise that has a tremendous impact on the world of biotechnology, personalised medicine, genome discovery and cancer treatment.

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Machine Learning

DBFE: Distribution-based feature extraction from copy number and structural variants in whole-genome data

M. Piernik, D. Brzezinski, P. Sztromwasser, K. Pacewicz, W. Majer-Burman, M. Gniot, D. Sielski, O. Bryzghalov, A. Wozna, P. Zawadzki

DOI

https://doi.org/10.1093/bioinformatics/btac513

Structural Variants

Machine Learning

Journal

Random Similarity Forests

M.Piernik, D.Brzezinski, P.Zawadzki

DOI

https://arxiv.org/abs/2204.05389

Machine Learning

Journal

1209P A new, whole-genome-based PARPi profiler (_ALICE) demonstrates unprecedented efficiency in patient selection for PARPi therapy

P. Zawadzki, A. Piotrowska, M. Wojtaszewska, P. Sztromwasser

DOI

https://doi.org/10.1016/j.annonc.2020.08.103

Machine Learning

PARP inhibitors

Journal

A study on using data clustering for feature extraction to improve the quality of classification

M. Piernik & T. Morzy

DOI

https://doi.org/10.1007/s10115-021-01572-6

Data Clusstering

Machine Learning

classification methods

Journal

The impact of data difficulty factors on classification of imbalanced and concept drifting data streams

D. Brzezinski, L.L. Minku, T. Pewinski, J. Stefanowski & A. Szumaczuk

DOI

https://doi.org/10.1007/s10115-021-01560-w

Machine Learning

classification methods

Journal

Recognizing and validating ligands with CheckMyBlob

D. Brzezinski, P.J. Porebski, M. Kowiel, J.M. Macnar, W.Minor

DOI

https://doi.org/10.1093/nar/gkab296

ligand identification

crystallography

Machine Learning

structural biology

Journal