| 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 | 492 | 2015 |
| 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 | 388 | 2008 |
| Comparative analysis of five protein-protein interaction corpora S Pyysalo, A Airola, J Heimonen, J Björne, F Ginter, T Salakoski BMC bioinformatics 9, 1-11, 2008 | 308 | 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 | 270 | 2009 |
| 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 | 202 | 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 | 174 | 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 | 168 | 2017 |
| 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 | 121 | 2008 |
| 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 | 120 | 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 |
| 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 | 97 | 2009 |
| Comparison of automatic summarisation methods for clinical free text notes H Moen, LM Peltonen, J Heimonen, A Airola, T Pahikkala, T Salakoski, ... Artificial intelligence in medicine 67, 25-37, 2016 | 85 | 2016 |
| 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 | 85 | 2009 |
| Learning with multiple pairwise kernels for drug bioactivity prediction A Cichonska, T Pahikkala, S Szedmak, H Julkunen, A Airola, M Heinonen, ... Bioinformatics 34 (13), i509-i518, 2018 | 78 | 2018 |
| Fast and simple gradient-based optimization for semi-supervised support vector machines F Gieseke, A Airola, T Pahikkala, O Kramer Neurocomputing 123, 23-32, 2014 | 74 | 2014 |
| Robust ECG R-peak detection using LSTM J Laitala, M Jiang, E Syrjälä, EK Naeini, A Airola, AM Rahmani, ND Dutt, ... Proceedings of the 35th annual ACM symposium on applied computing, 1104-1111, 2020 | 69 | 2020 |
| Predicting patient acuity from electronic patient records E Kontio, A Airola, T Pahikkala, H Lundgren-Laine, K Junttila, ... Journal of Biomedical Informatics 51, 35-40, 2014 | 63 | 2014 |
| EXTRACTING CONTEXTUALIZED COMPLEX BIOLOGICAL EVENTS WITH RICH GRAPH‐BASED FEATURE SETS J Björne, J Heimonen, F Ginter, A Airola, T Pahikkala, T Salakoski Computational Intelligence 27 (4), 541-557, 2011 | 61 | 2011 |
| Machine learning to automate the assignment of diagnosis codes to free-text radiology reports: a method description H Suominen, F Ginter, S Pyysalo, A Airola, T Pahikkala, S Salanter, ... Proceedings of the ICML/UAI/COLT workshop on machine learning for health …, 2008 | 54 | 2008 |
| Synthetic minority oversampling of vital statistics data with generative adversarial networks A Koivu, M Sairanen, A Airola, T Pahikkala Journal of the American Medical Informatics Association 27 (11), 1667-1674, 2020 | 51 | 2020 |
Tapio PahikkalaProfessor, University of turku, FinlandVerified email at utu.fi
