Meelis Kull
Cited by
Cited by
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
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
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
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
Precision-recall-gain curves: PR analysis done right
P Flach, M Kull
Advances in neural information processing systems, 838-846, 2015
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
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
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), R80, 2010
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
Fast approximate hierarchical clustering using similarity heuristics
M Kull, J Vilo
BioData mining 1, 9, 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
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
Patterns of dataset shift
M Kull, P Flach
First International Workshop on Learning over Multiple Contexts (LMCE) at …, 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
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
Versatile decision trees for learning over multiple contexts
R Al-Otaibi, RBC Prudêncio, M Kull, P Flach
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2015
Probabilistic sensor fusion for ambient assisted living
T Diethe, N Twomey, M Kull, P Flach, I Craddock
arXiv preprint arXiv:1702.01209, 2017
VisHiC—hierarchical functional enrichment analysis of microarray data
D Krushevskaya, H Peterson, J Reimand, M Kull, J Vilo
Nucleic acids research 37 (suppl_2), W587-W592, 2009
Reframing in context: A systematic approach for model reuse in machine learning
J Hernández-Orallo, A Martínez-Usó, RBC Prudêncio, M Kull, P Flach, ...
AI Communications 29 (5), 551-566, 2016
Subgroup discovery with proper scoring rules
H Song, M Kull, P Flach, G Kalogridis
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2016
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