g: Profiler—a web-based toolset for functional profiling of gene lists from large-scale experiments J Reimand, M Kull, H Peterson, J Hansen, J Vilo Nucleic acids research 35 (suppl_2), W193-W200, 2007 | 781 | 2007 |
Mining for coexpression across hundreds of datasets using novel rank aggregation and visualization methods P Adler, R Kolde, M Kull, A Tkachenko, H Peterson, J Reimand, J Vilo Genome biology 10 (12), R139, 2009 | 155 | 2009 |
Expression Profiler: next generation—an online platform for analysis of microarray data M Kapushesky, P Kemmeren, AC Culhane, S Durinck, J Ihmels, C Körner, ... Nucleic acids research 32 (suppl_2), W465-W470, 2004 | 147 | 2004 |
Precision-recall-gain curves: PR analysis done right P Flach, M Kull Advances in neural information processing systems 28, 838-846, 2015 | 137 | 2015 |
ASTD: the alternative splicing and transcript diversity database G Koscielny, V Le Texier, C Gopalakrishnan, V Kumanduri, JJ Riethoven, ... Genomics 93 (3), 213-220, 2009 | 122 | 2009 |
The SPHERE challenge: Activity recognition with multimodal sensor data N Twomey, T Diethe, M Kull, H Song, M Camplani, S Hannuna, X Fafoutis, ... arXiv preprint arXiv:1603.00797, 2016 | 59 | 2016 |
Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers M Kull, T Silva Filho, P Flach Artificial Intelligence and Statistics, 623-631, 2017 | 50 | 2017 |
Cost-sensitive boosting algorithms: Do we really need them? N Nikolaou, N Edakunni, M Kull, P Flach, G Brown Machine Learning 104 (2-3), 359-384, 2016 | 41 | 2016 |
Comprehensive transcriptome analysis of mouse embryonic stem cell adipogenesis unravels new processes of adipocyte development N Billon, R Kolde, J Reimand, MC Monteiro, M Kull, H Peterson, ... Genome biology 11 (8), 1-16, 2010 | 36 | 2010 |
Beyond sigmoids: How to obtain well-calibrated probabilities from binary classifiers with beta calibration M Kull, TM Silva Filho, P Flach Electronic Journal of Statistics 11 (2), 5052-5080, 2017 | 35 | 2017 |
Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with dirichlet calibration M Kull, MP Nieto, M Kängsepp, T Silva Filho, H Song, P Flach Advances in neural information processing systems, 12316-12326, 2019 | 33 | 2019 |
Fast approximate hierarchical clustering using similarity heuristics M Kull, J Vilo BioData mining 1, 9, 2008 | 32 | 2008 |
Novel decompositions of proper scoring rules for classification: Score adjustment as precursor to calibration M Kull, P Flach Joint European Conference on Machine Learning and Knowledge Discovery in …, 2015 | 28 | 2015 |
Patterns of dataset shift M Kull, P Flach First International Workshop on Learning over Multiple Contexts (LMCE) at …, 2014 | 22 | 2014 |
Reliability maps: a tool to enhance probability estimates and improve classification accuracy M Kull, PA Flach Joint European Conference on Machine Learning and Knowledge Discovery in …, 2014 | 14 | 2014 |
CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories F Martínez-Plumed, L Contreras-Ochando, C Ferri, JH Orallo, M Kull, ... IEEE Transactions on Knowledge and Data Engineering, 2019 | 13 | 2019 |
Releasing ehealth analytics into the wild: lessons learnt from the sphere project T Diethe, M Holmes, M Kull, M Perello Nieto, K Sokol, H Song, E Tonkin, ... Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018 | 12 | 2018 |
Global transcriptomic analysis of murine embryonic stem cell‐derived brachyury+ (T) cells MX Doss, V Wagh, H Schulz, M Kull, R Kolde, K Pfannkuche, T Nolden, ... Genes to Cells 15 (3), 209-228, 2010 | 12 | 2010 |
Distribution calibration for regression H Song, T Diethe, M Kull, P Flach Proceedings of the 36th International Conference on Machine Learning (ICML …, 2019 | 11 | 2019 |
Probabilistic sensor fusion for ambient assisted living T Diethe, N Twomey, M Kull, P Flach, I Craddock arXiv preprint arXiv:1702.01209, 2017 | 11 | 2017 |