| Clustering XML documents by patterns M Piernik, D Brzezinski, T Morzy Knowledge and Information Systems 46, 185-212, 2016 | 35 | 2016 |
| A study on using data clustering for feature extraction to improve the quality of classification M Piernik, T Morzy Knowledge and Information Systems 63 (7), 1771-1805, 2021 | 29 | 2021 |
| XML clustering: a review of structural approaches M Piernik, D Brzezinski, T Morzy, A Lesniewska The Knowledge Engineering Review 30 (3), 297-323, 2015 | 28 | 2015 |
| XCleaner: A new method for clustering XML documents by structure D Brzeziński, A Leśniewska, T Morzy, M Piernik Control and Cybernetics 40 (3), 877-891, 2011 | 13 | 2011 |
| Structural XML classification in concept drifting data streams D Brzezinski, M Piernik New Generation Computing 33, 345-366, 2015 | 10 | 2015 |
| Healthcare integration platform J Brzeziński, S Czajka, J Kobusiński, M Piernik 2011 5th International Symposium on Medical Information and Communication …, 2011 | 10 | 2011 |
| Partial tree-edit distance M Piernik, T Morzy, A Nikolaus, M Pawlik Poznan University of Technology, Tech. Rep. RA-10/2013, 2013 | 5 | 2013 |
| Adaptive XML stream classification using partial tree-edit distance D Brzezinski, M Piernik Foundations of Intelligent Systems: 21st International Symposium, ISMIS 2014 …, 2014 | 4 | 2014 |
| Partial tree-edit distance: a solution to the default class problem in pattern-based tree classification M Piernik, T Morzy Advances in Knowledge Discovery and Data Mining: 21st Pacific-Asia …, 2017 | 3 | 2017 |
| Validation of HER2 Status in Whole Genome Sequencing Data of Breast Cancers with the Ploidy-Corrected Copy Number Approach M Wojtaszewska, R Stępień, A Woźna, M Piernik, P Sztromwasser, ... Molecular Diagnosis & Therapy, 1-12, 2022 | 2 | 2022 |
| DBFE: distribution-based feature extraction from structural variants in whole-genome data M Piernik, D Brzezinski, P Sztromwasser, K Pacewicz, W Majer-Burman, ... Bioinformatics 38 (19), 4466-4473, 2022 | 1 | 2022 |
| Random Similarity Forests M Piernik, D Brzezinski, P Zawadzki Joint European Conference on Machine Learning and Knowledge Discovery in …, 2022 | 1 | 2022 |
| Improved response prediction to immune checkpoint inhibition by combining TMB and WGS-based genomic features in NSCLC. K Pacewicz, A Kraszewski, M Medzin, P Nawrocka-Muszynska, ... Journal of Clinical Oncology 40 (16_suppl), e21077-e21077, 2022 | 1 | 2022 |
| Pattern-based clustering and classification of XML data M Piernik | 1 | 2015 |
| 1082P Improved response prediction to immune checkpoint inhibitors by combining TMB and WGS-driven genomic features in NSCLC P Nawrocka-Muszyńska, K Pacewicz, M Mędzin, P Sztromwasser, ... Annals of Oncology 33, S1047-S1048, 2022 | | 2022 |
| 15P Integration of whole genome data with machine learning technology in breast cancer subtyping W Majer-Burman, K Pacewicz, M Meler, M Gniot, D Sielski, M Piernik, ... Annals of Oncology 33, S130, 2022 | | 2022 |
| 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, ... bioRxiv, 2022.02. 09.479712, 2022 | | 2022 |
| Validation of HER2 status in whole genome sequencing data of breast cancers with AI-driven, ploidy-corrected approach W Marzena, S Rafał, W Alicja, P Maciej, D Maciej, G Michał, S Sławomir, ... medRxiv, 2021.08. 30.21258379, 2021 | | 2021 |
| 1134P Personalized medicine in advanced breast cancer: AI-driven genomic test for CDK4/6 treatment response prediction P Zawadzki, A Woźna, P Sztromwasser, W Majer-Burman, R Stępień, ... Annals of Oncology 32, S925, 2021 | | 2021 |
| Assessing the effectiveness of sequences of treatments using sequential patterns M Piernik, J Solomiewicz, A Jachnik Artificial Intelligence in Medicine: 17th Conference on Artificial …, 2019 | | 2019 |
Dariusz BrzezinskiPoznan University of TechnologyVerified email at cs.put.poznan.pl
