Markus Heinonen
Cited by
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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
Metabolite identification and molecular fingerprint prediction via machine learning
M Heinonen, H Shen, N Zamboni, J Rousu
Bioinformatics 28 (18), 2333-2341, 2012
Non-Stationary Spectral Kernels
S Remes, M Heinonen, S Kaski
NIPS 30, 4642-4651, 2017
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
M Heinonen, H Mannerström, J Rousu, S Kaski, H Lähdesmäki
AISTATS 51, 732-740, 2016
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
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
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
Genome wide analysis of protein production load in Trichoderma reesei
TM Pakula, H Nygren, D Barth, M Heinonen, S Castillo, M Penttilä, ...
Biotechnology for biofuels 9 (1), 132, 2016
Metabolite identification through machine learning—Tackling CASMI challenge using fingerID
H Shen, N Zamboni, M Heinonen, J Rousu
Metabolites 3 (2), 484-505, 2013
Ab initio prediction of molecular fragments from tandem mass spectrometry data
M Heinonen, A Rantanen, T Mielikäinen, E Pitkänen, J Kokkonen, ...
German Conference on Bioinformatics 83, 40-53, 2006
Random fourier features for operator-valued kernels
R Brault, M Heinonen, F Buc
Asian Conference on Machine Learning 63, 110-125, 2016
Deep learning with differential Gaussian process flows
P Hegde, M Heinonen, H Lähdesmäki, S Kaski
AISTATS 89, 1812-1821, 2019
Deep convolutional gaussian processes
K Blomqvist, S Kaski, M Heinonen
ECML, 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
Learning unknown ODE models with Gaussian processes
M Heinonen, C Yildiz, H Mannerström, J Intosalmi, H Lähdesmäki
ICML 80, 1959-1968, 2018
Structured output prediction of anti-cancer drug activity
H Su, M Heinonen, J Rousu
IAPR International Conference on Pattern Recognition in Bioinformatics, 38-49, 2010
Efficient Path Kernels for Reaction Function Prediction
M Heinonen, N Välimäki, V Mäkinen, J Rousu
BIOINFORMATICS 2012 - Proceedings of the International Conference on …, 2012
ODE2VAE: Deep generative second order ODEs with Bayesian neural networks
C Yildiz, M Heinonen, H Lahdesmaki
Advances in Neural Information Processing Systems, 13412-13421, 2019
mGPfusion: predicting protein stability changes with Gaussian process kernel learning and data fusion
E Jokinen, M Heinonen, H Lähdesmäki
Bioinformatics 34 (13), i274-i283, 2018
Harmonizable mixture kernels with variational Fourier features
Z Shen, M Heinonen, S Kaski
AISTATS 89, 3273-3282, 2019
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