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Tapio Pahikkala
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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
4342015
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
4072014
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
3762008
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
2652009
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
2182017
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
1892016
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
1862011
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
1612017
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
1602014
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
1282017
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
1272015
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
1172008
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
1152019
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
1142013
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
1072007
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
1032017
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
1002019
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
942009
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
842009
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
832015
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Articles 1–20