Markus Heinonen
Markus Heinonen
Academy research Fellow, Aalto University
Verified email at - Homepage
Cited by
Cited by
Metabolite identification and molecular fingerprint prediction via machine learning
M Heinonen, H Shen, N Zamboni, J Rousu
Bioinformatics 28 (18), 2333-2341, 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
ODEVAE: Deep generative second order ODEs with Bayesian neural networks
Ç Yıldız, M Heinonen, H Lähdesmäki
NeurIPS, 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
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
M Heinonen, H Mannerström, J Rousu, S Kaski, H Lähdesmäki
AISTATS 51, 732-740, 2016
Non-Stationary Spectral Kernels
S Remes, M Heinonen, S Kaski
NIPS 30, 4642-4651, 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
Learning unknown ODE models with Gaussian processes
M Heinonen, C Yildiz, H Mannerström, J Intosalmi, H Lähdesmäki
ICML 80, 1959-1968, 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
Deep convolutional gaussian processes
K Blomqvist, S Kaski, M Heinonen
ECML, 2019
Learning continuous-time pdes from sparse data with graph neural networks
V Iakovlev, M Heinonen, H Lähdesmäki
arXiv preprint arXiv:2006.08956, 2020
Generative modelling with inverse heat dissipation
S Rissanen, M Heinonen, A Solin
arXiv preprint arXiv:2206.13397, 2022
Continuous-time model-based reinforcement learning
C Yildiz, M Heinonen, H Lähdesmäki
International Conference on Machine Learning, 12009-12018, 2021
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
Determining epitope specificity of T cell receptors with TCRGP
E Jokinen, J Huuhtanen, S Mustjoki, M Heinonen, H Lähdesmäki
BioRxiv, 542332, 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
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
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
Metabolite identification through machine learning—tackling CASMI challenge using FingerID
H Shen, N Zamboni, M Heinonen, J Rousu
Metabolites 3 (2), 484-505, 2013
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