A community effort to assess and improve drug sensitivity prediction algorithms JC Costello, LM Heiser, E Georgii, M Gönen, MP Menden, NJ Wang, ... Nature biotechnology 32 (12), 1202-1212, 2014 | 798 | 2014 |
Identifying differentially expressed transcripts from RNA-seq data with biological variation P Glaus, A Honkela, M Rattray Bioinformatics 28 (13), 1721-1728, 2012 | 268 | 2012 |
Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes JA Lees, M Vehkala, N Välimäki, SR Harris, C Chewapreecha, ... Nature communications 7 (1), 12797, 2016 | 225 | 2016 |
Bayesian non-linear independent component analysis by multi-layer perceptrons H Lappalainen, A Honkela Advances in independent component analysis, 93-121, 2000 | 164 | 2000 |
Approximate Riemannian conjugate gradient learning for fixed-form variational Bayes A Honkela, T Raiko, M Kuusela, M Tornio, J Karhunen The Journal of Machine Learning Research 11, 3235-3268, 2010 | 134 | 2010 |
Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities P Gao, A Honkela, M Rattray, ND Lawrence Bioinformatics 24 (16), i70-i75, 2008 | 132 | 2008 |
Model-based method for transcription factor target identification with limited data A Honkela, C Girardot, EH Gustafson, YH Liu, EEM Furlong, ... Proceedings of the National Academy of Sciences 107 (17), 7793-7798, 2010 | 129 | 2010 |
Computing tight differential privacy guarantees using FFT A Koskela, J Jälkö, A Honkela International Conference on Artificial Intelligence and Statistics, 2560-2569, 2020 | 119 | 2020 |
Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays A Honkela, J Peltonen, H Topa, I Charapitsa, F Matarese, K Grote, ... Proceedings of the National Academy of Sciences 112 (42), 13115-13120, 2015 | 100 | 2015 |
Variational learning and bits-back coding: an information-theoretic view to Bayesian learning A Honkela, H Valpola IEEE transactions on Neural Networks 15 (4), 800-810, 2004 | 93 | 2004 |
A generative approach for image-based modeling of tumor growth BH Menze, K Van Leemput, A Honkela, E Konukoglu, MA Weber, ... Information Processing in Medical Imaging: 22nd International Conference …, 2011 | 82 | 2011 |
Unsupervised variational Bayesian learning of nonlinear models A Honkela, H Valpola Advances in neural information processing systems 17, 2004 | 72 | 2004 |
Differentially private Bayesian learning on distributed data M Heikkilä, E Lagerspetz, S Kaski, K Shimizu, S Tarkoma, A Honkela Advances in neural information processing systems, 3226-3235, 2017 | 71 | 2017 |
Seasonal variation in genome-wide DNA methylation patterns and the onset of seasonal timing of reproduction in great tits HM Viitaniemi, I Verhagen, ME Visser, A Honkela, K Van Oers, A Husby Genome Biology and Evolution 11 (3), 970-983, 2019 | 70 | 2019 |
Tight differential privacy for discrete-valued mechanisms and for the subsampled Gaussian mechanism using FFT A Koskela, J Jälkö, L Prediger, A Honkela International Conference on Artificial Intelligence and Statistics, 3358-3366, 2021 | 69* | 2021 |
On-line variational Bayesian learning A Honkela, H Valpola 4th International Symposium on Independent Component Analysis and Blind …, 2003 | 63 | 2003 |
Nonlinear independent component analysis using ensemble learning: Experiments and discussion H Valpola, X Giannakopoulos, A Honkela, J Karhunen Proc. Second International Workshop on Independent Component Analysis and …, 2000 | 62* | 2000 |
Differentially private variational inference for non-conjugate models J Jälkö, O Dikmen, A Honkela arXiv preprint arXiv:1610.08749, 2016 | 60 | 2016 |
Natural conjugate gradient in variational inference A Honkela, M Tornio, T Raiko, J Karhunen Neural Information Processing: 14th International Conference, ICONIP 2007 …, 2008 | 54 | 2008 |
Learning rate adaptation for differentially private learning A Koskela, A Honkela International Conference on Artificial Intelligence and Statistics, 2465-2475, 2020 | 52* | 2020 |