| Genomic characterization of relapsed acute myeloid leukemia reveals novel putative therapeutic targets S Stratmann, SA Yones, M Mayrhofer, N Norgren, A Skaftason, J Sun, ... Blood advances 5 (3), 900-912, 2021 | 42 | 2021 |
| 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 |
| 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 |
| Studies of liver tissue identify functional gene regulatory elements associated to gene expression, type 2 diabetes, and other metabolic diseases M Cavalli, N Baltzer, G Pan, JR Bárcenas Walls, ... Human Genomics 13, 1-8, 2019 | 8 | 2019 |
| funMotifs: Tissue-specific transcription factor motifs HM Umer, K Smolinska-Garbulowska, N Marzouka, Z Khaliq, C Wadelius, ... BioRxiv, 683722, 2019 | 6 | 2019 |
| 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 |
| Functional annotation of noncoding mutations in cancer HM Umer, K Smolinska, J Komorowski, C Wadelius Life science alliance 4 (9), 2021 | 4 | 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 |
| 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 |
| EMQIT: a machine learning approach for energy based PWM matrix quality improvement K Smolinska, M Pacholczyk Biology Direct 12, 1-8, 2017 | 2 | 2017 |
| Elucidation of complex diseases by machine learning K Smolinska Garbulowska Acta Universitatis Upsaliensis, 2021 | | 2021 |
| SUPPLEMENTARY MATERIAL: funMotifs: Tissue-specific transcription factor motifs K Smolinska, HM Umer, Z Khaliq, C Wadelius, J Komorowski | | 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 |
| SUPPLEMENTAL INFORMATION FOR: Genomic characterization of adult and pediatric relapsed acute myeloid leukemia reveals novel therapeutic targets S Stratmann, SA Yones, M Mayrhofer, N Norgren, A Skaftason, J Sun, ... | | 2020 |
| funMotifs: Tissue-specific transcription factor motifs K Smolinska, HM Umer, Z Khaliq, C Wadelius, J Komorowski | | 2018 |
| A gradually built up immune response specifies protection against Simian Immunodeficiency Virus infection in Rhesus Macaques Z Khaliq, F Barrenäs, K Smolinska, L Aarreberg, V Chamcha, L Law, ... | | 2017 |
| Computational approach for modeling and testing NF-kB binding sites M Pacholczyk, K Smolińska, M Iwanaszko, M Kimmel | | 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, ... | | |
| Improved computational technique for modeling and testing transcription factor binding sites M PACHOLCZYK, K SMOLINSKA, M KIMMEL | | |
