Toward more realistic drug–target interaction predictions T Pahikkala, A Airola, S Pietilä, S Shakyawar, A Szwajda, J Tang, ... Briefings in bioinformatics 16 (2), 325-337, 2015 | 452 | 2015 |
Using ant colony system to consolidate VMs for green cloud computing F Farahnakian, A Ashraf, T Pahikkala, P Liljeberg, J Plosila, I Porres, ... IEEE transactions on services computing 8 (2), 187-198, 2014 | 415 | 2014 |
All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning A Airola, S Pyysalo, J Björne, T Pahikkala, F Ginter, T Salakoski BMC bioinformatics 9, 1-12, 2008 | 379 | 2008 |
Extracting complex biological events with rich graph-based feature sets J Björne, J Heimonen, F Ginter, A Airola, T Pahikkala, T Salakoski Proceedings of the BioNLP 2009 Workshop Companion Volume for Shared Task, 10-18, 2009 | 266 | 2009 |
HiCH: Hierarchical fog-assisted computing architecture for healthcare IoT I Azimi, A Anzanpour, AM Rahmani, T Pahikkala, M Levorato, P Liljeberg, ... ACM Transactions on Embedded Computing Systems (TECS) 16 (5s), 1-20, 2017 | 228 | 2017 |
Energy-aware VM consolidation in cloud data centers using utilization prediction model F Farahnakian, T Pahikkala, P Liljeberg, J Plosila, NT Hieu, H Tenhunen IEEE Transactions on Cloud Computing 7 (2), 524-536, 2016 | 199 | 2016 |
An experimental comparison of cross-validation techniques for estimating the area under the ROC curve A Airola, T Pahikkala, W Waegeman, B De Baets, T Salakoski Computational Statistics & Data Analysis 55 (4), 1828-1844, 2011 | 192 | 2011 |
Regularized machine learning in the genetic prediction of complex traits S Okser, T Pahikkala, A Airola, T Salakoski, S Ripatti, T Aittokallio PLoS genetics 10 (11), e1004754, 2014 | 164 | 2014 |
Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open … J Guinney, T Wang, TD Laajala, KK Winner, JC Bare, EC Neto, SA Khan, ... The Lancet Oncology 18 (1), 132-142, 2017 | 161 | 2017 |
Estimating the prediction performance of spatial models via spatial k-fold cross validation J Pohjankukka, T Pahikkala, P Nevalainen, J Heikkonen International Journal of Geographical Information Science 31 (10), 2001-2019, 2017 | 137 | 2017 |
Utilization prediction aware VM consolidation approach for green cloud computing F Farahnakian, T Pahikkala, P Liljeberg, J Plosila, H Tenhunen 2015 IEEE 8th International Conference on Cloud Computing, 381-388, 2015 | 131 | 2015 |
A graph kernel for protein-protein interaction extraction A Airola, S Pyysalo, J Björne, T Pahikkala, F Ginter, T Salakoski Proceedings of the workshop on current trends in biomedical natural language …, 2008 | 119 | 2008 |
Missing data resilient decision-making for healthcare IoT through personalization: A case study on maternal health I Azimi, T Pahikkala, AM Rahmani, H Niela-Vilén, A Axelin, P Liljeberg Future Generation Computer Systems 96, 297-308, 2019 | 118 | 2019 |
Energy aware consolidation algorithm based on k-nearest neighbor regression for cloud data centers F Farahnakian, T Pahikkala, P Liljeberg, J Plosila 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing …, 2013 | 117 | 2013 |
Computational-experimental approach to drug-target interaction mapping: a case study on kinase inhibitors A Cichonska, B Ravikumar, E Parri, S Timonen, T Pahikkala, A Airola, ... PLoS computational biology 13 (8), e1005678, 2017 | 109 | 2017 |
Learning to rank with pairwise regularized least-squares T Pahikkala, E Tsivtsivadze, A Airola, J Boberg, T Salakoski SIGIR 2007 workshop on learning to rank for information retrieval 80, 27-33, 2007 | 107 | 2007 |
Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization J Toivonen, I Montoya Perez, P Movahedi, H Merisaari, M Pesola, ... PloS one 14 (7), e0217702, 2019 | 104 | 2019 |
A comparison of AUC estimators in small-sample studies A Airola, T Pahikkala, W Waegeman, B De Baets, T Salakoski Machine learning in systems biology, 3-13, 2009 | 94 | 2009 |
Mathematical models for diffusion‐weighted imaging of prostate cancer using b values up to 2000 s/mm2: Correlation with Gleason score and repeatability of … J Toivonen, H Merisaari, M Pesola, P Taimen, PJ Boström, T Pahikkala, ... Magnetic resonance in medicine 74 (4), 1116-1124, 2015 | 85 | 2015 |
An efficient algorithm for learning to rank from preference graphs T Pahikkala, E Tsivtsivadze, A Airola, J Järvinen, J Boberg Machine Learning 75, 129-165, 2009 | 84 | 2009 |