Metabolite identification and molecular fingerprint prediction via machine learning M Heinonen, H Shen, N Zamboni, J Rousu
Bioinformatics 28 (18), 2333-2341, 2012
199 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
187 2018 ODE Ç Yıldız, M Heinonen, H Lähdesmäki
NeurIPS, 2019
182 * 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
161 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
133 2016 Non-Stationary Spectral Kernels S Remes, M Heinonen, S Kaski
NIPS 30, 4642-4651, 2017
119 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
85 2021 Learning unknown ODE models with Gaussian processes M Heinonen, C Yildiz, H Mannerström, J Intosalmi, H Lähdesmäki
ICML 80, 1959-1968, 2018
79 2018 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
73 2018 Deep convolutional gaussian processes K Blomqvist, S Kaski, M Heinonen
ECML, 2019
66 2019 Learning continuous-time pdes from sparse data with graph neural networks V Iakovlev, M Heinonen, H Lähdesmäki
arXiv preprint arXiv:2006.08956, 2020
64 2020 Generative modelling with inverse heat dissipation S Rissanen, M Heinonen, A Solin
arXiv preprint arXiv:2206.13397, 2022
57 2022 Continuous-time model-based reinforcement learning C Yildiz, M Heinonen, H Lähdesmäki
International Conference on Machine Learning, 12009-12018, 2021
50 2021 Deep learning with differential Gaussian process flows P Hegde, M Heinonen, H Lähdesmäki, S Kaski
AISTATS 89, 1812-1821, 2019
46 2019 Determining epitope specificity of T cell receptors with TCRGP E Jokinen, J Huuhtanen, S Mustjoki, M Heinonen, H Lähdesmäki
BioRxiv, 542332, 2019
44 2019 Random fourier features for operator-valued kernels R Brault, M Heinonen, F Buc
Asian Conference on Machine Learning 63, 110-125, 2016
44 2016 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 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
38 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
37 2013 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
36 2018 
Juho RousuProfessor, Department of Computer Science, Aalto UniversityDirección de correo verificada de aalto.fi
