Maciej Piernik
Cited by
Cited by
Clustering XML documents by patterns
M Piernik, D Brzezinski, T Morzy
Knowledge and Information Systems 46, 185-212, 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
XML clustering: a review of structural approaches
M Piernik, D Brzezinski, T Morzy, A Lesniewska
The Knowledge Engineering Review 30 (3), 297-323, 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
Structural XML classification in concept drifting data streams
D Brzezinski, M Piernik
New Generation Computing 33, 345-366, 2015
Healthcare integration platform
J Brzeziński, S Czajka, J Kobusiński, M Piernik
2011 5th International Symposium on Medical Information and Communication …, 2011
Adaptive XML stream classification using partial tree-edit distance
D Brzezinski, M Piernik
Foundations of Intelligent Systems: 21st International Symposium, ISMIS 2014 …, 2014
Partial tree-edit distance
M Piernik, T Morzy, A Nikolaus, M Pawlik
Poznan University of Technology, Tech. Rep. RA-10/2013, 2013
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
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
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
Pattern-based clustering and classification of XML data
M Piernik
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
Random Similarity Forests
M Piernik, D Brzezinski, P Zawadzki
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2022
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
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
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
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
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
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
The system can't perform the operation now. Try again later.
Articles 1–20