| R. ROSETTA: an interpretable machine learning framework M Garbulowski, K Diamanti, K Smolińska, N Baltzer, P Stoll, S Bornelöv, ... BMC bioinformatics 22, 1-18, 2021 | 21 | 2021 |
| Transcriptomic analysis reveals proinflammatory signatures associated with acute myeloid leukemia progression S Stratmann, SA Yones, M Garbulowski, J Sun, A Skaftason, M Mayrhofer, ... Blood advances 6 (1), 152-164, 2022 | 18 | 2022 |
| Interpretable machine learning reveals dissimilarities between subtypes of autism spectrum disorder M Garbulowski, K Smolinska, K Diamanti, G Pan, K Maqbool, L Feuk, ... Frontiers in Genetics 12, 618277, 2021 | 10 | 2021 |
| RareVariantVis: new tool for visualization of causative variants in rare monogenic disorders using whole genome sequencing data T Stokowy, M Garbulowski, T Fiskerstrand, R Holdhus, K Labun, ... Bioinformatics 32 (19), 3018-3020, 2016 | 8 | 2016 |
| Coalescence computations for large samples drawn from populations of time-varying sizes A Polanski, A Szczesna, M Garbulowski, M Kimmel PLoS One 12 (2), e0170701, 2017 | 7 | 2017 |
| VisuNet: an interactive tool for rule network visualization of rule-based learning models K Smolinska, M Garbulowski, K Diamanti, X Davoy, SOO Anyango, ... | 5 | 2021 |
| Machine learning-based analysis of glioma grades reveals Co-enrichment M Garbulowski, K Smolinska, U Çabuk, SA Yones, L Celli, EN Yaz, ... Cancers 14 (4), 1014, 2022 | 3 | 2022 |
| R. ROSETTA: an interpretable machine learning framework M Garbulowski, K Diamanti, K Smolińska, N Baltzer, P Stoll, S Bornelöv, ... bioRxiv, 625905, 2019 | 2 | 2019 |
| ROSETTA: an R package for analysis of rule-based classification models M Garbulowski, K Diamanti, K Smolińska, P Stoll, S Bornelöv, A Øhrn, ... Submitted, 2018 | 2 | 2018 |
| Patterns in big data bioinformatics: Understanding complex diseases with interpretable machine learning M Garbulowski Acta Universitatis Upsaliensis, 2021 | | 2021 |
| SUPPLEMENTARY MATERIAL: VisuNet: an interactive tool for rule network visualization of rule-based learning models K Smolinska Garbulowska, M Garbulowski, K Diamanti, X Davoy, ... | | 2021 |
| SUPPLEMENTARY MATERIAL: Machine learning-based analysis of glioma grades reveals co-enrichment M Garbulowski, K Smolinska Garbulowska, U Çabuk, SA Yones, L Celli, ... | | 2021 |
| SUPPLEMENTARY MATERIAL: Transcriptomic analysis reveals pro-inflammatory signatures associated with acute myeloid leukemia progression S Stratmann, SA Yones, M Garbulowski, J Sun, A Skaftason, M Mayrhofer, ... | | 2021 |
| SUPPLEMENTAL INFORMATION FOR: Transcriptomic analysis reveals pro-inflammatory signatures associated with acute myeloid leukemia progression S Stratmann, SA Yones, M Garbulowski, J Sun, A Skaftason, M Mayrhofer, ... | | 2020 |
| Consensus Approach for Detection of Cancer Somatic Mutations K Sieradzka, K Leszczorz, M Garbulowski, A Polanski Man-Machine Interactions 5: 5th International Conference on Man-Machine …, 2018 | | 2018 |
| Impact of the ultrasonic preconditioning onto sedimentation process R Sancewicz, M Lemanowicz, A Gierczycki, M Garbulowski Inżynieria i Aparatura Chemiczna, 2015 | | 2015 |
| A model of genome length estimation based on k-mers detection M Garbulowski, A Polański Studia Informatica 36 (4), 5--16, 2015 | | 2015 |
| A system for simulation of DNA coverage in shotgun sequencing processes M Garbulowski, A Polański Pomiary Automatyka Kontrola 60, 2014 | | 2014 |
| Supplementary Materials VisuNet: an interactive tool for rule network visualization of rule-based learning models K Smolinska, M Garbulowski, K Diamanti, X Davoy, S OO, FB Anyango, ... | | |
