| Metabolite identification and molecular fingerprint prediction via machine learning M Heinonen, H Shen, N Zamboni, J Rousu Bioinformatics 28 (18), 2333-2341, 2012 | 211 | 2012 |
| Flex ddG: Rosetta ensemble-based estimation of changes in protein–protein binding affinity upon mutation KA Barlow, S Ó Conchúir, S Thompson, P Suresh, JE Lucas, M Heinonen, ... The Journal of Physical Chemistry B 122 (21), 5389-5399, 2018 | 208 | 2018 |
| ODEVAE: Deep generative second order ODEs with Bayesian neural networks Ç Yıldız, M Heinonen, H Lähdesmäki NeurIPS, 2019 | 203* | 2019 |
| FiD: a software for ab initio structural identification of product ions from tandem mass spectrometric data M Heinonen, A Rantanen, T Mielikäinen, J Kokkonen, J Kiuru, RA Ketola, ... Rapid Communications in Mass Spectrometry 22 (19), 3043-3052, 2008 | 164 | 2008 |
| Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo M Heinonen, H Mannerström, J Rousu, S Kaski, H Lähdesmäki AISTATS 51, 732-740, 2016 | 139 | 2016 |
| Non-Stationary Spectral Kernels S Remes, M Heinonen, S Kaski NIPS 30, 4642-4651, 2017 | 132 | 2017 |
| Predicting recognition between T cell receptors and epitopes with TCRGP E Jokinen, J Huuhtanen, S Mustjoki, M Heinonen, H Lähdesmäki PLoS computational biology 17 (3), e1008814, 2021 | 97 | 2021 |
| Learning unknown ODE models with Gaussian processes M Heinonen, C Yildiz, H Mannerström, J Intosalmi, H Lähdesmäki ICML 80, 1959-1968, 2018 | 88 | 2018 |
| Deep convolutional gaussian processes K Blomqvist, S Kaski, M Heinonen ECML, 2019 | 76 | 2019 |
| 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 | 76 | 2018 |
| Learning continuous-time pdes from sparse data with graph neural networks V Iakovlev, M Heinonen, H Lähdesmäki arXiv preprint arXiv:2006.08956, 2020 | 72 | 2020 |
| Generative modelling with inverse heat dissipation S Rissanen, M Heinonen, A Solin arXiv preprint arXiv:2206.13397, 2022 | 70 | 2022 |
| Continuous-time model-based reinforcement learning C Yildiz, M Heinonen, H Lähdesmäki International Conference on Machine Learning, 12009-12018, 2021 | 59 | 2021 |
| Random fourier features for operator-valued kernels R Brault, M Heinonen, F Buc Asian Conference on Machine Learning 63, 110-125, 2016 | 56 | 2016 |
| Deep learning with differential Gaussian process flows P Hegde, M Heinonen, H Lähdesmäki, S Kaski AISTATS 89, 1812-1821, 2019 | 49 | 2019 |
| Determining epitope specificity of T cell receptors with TCRGP E Jokinen, J Huuhtanen, S Mustjoki, M Heinonen, H Lähdesmäki BioRxiv, 542332, 2019 | 46 | 2019 |
| Computing atom mappings for biochemical reactions without subgraph isomorphism M Heinonen, S Lappalainen, T Mielikäinen, J Rousu Journal of Computational Biology 18 (1), 43-58, 2011 | 42 | 2011 |
| Learning stochastic differential equations with gaussian processes without gradient matching C Yildiz, M Heinonen, J Intosalmi, H Mannerstrom, H Lahdesmaki 2018 IEEE 28th International Workshop on Machine Learning for Signal …, 2018 | 41 | 2018 |
| Detecting time periods of differential gene expression using Gaussian processes: an application to endothelial cells exposed to radiotherapy dose fraction M Heinonen, O Guipaud, F Milliat, V Buard, B Micheau, G Tarlet, ... Bioinformatics 31, 728-735, 2015 | 40 | 2015 |
| Metabolite identification through machine learning—tackling CASMI challenge using FingerID H Shen, N Zamboni, M Heinonen, J Rousu Metabolites 3 (2), 484-505, 2013 | 38 | 2013 |
Juho RousuProfessor, Department of Computer Science, Aalto UniversityVerified email at aalto.fi
