Markus Heinonen

Research Fellow

Research outputs

  1. 2019
  2. Published

    Bayesian metabolic flux analysis reveals intracellular flux couplings

    Heinonen, M., Osmala, M., Mannerström, H., Wallenius, J., Kaski, S., Rousu, J. & Lähdesmäki, H., 15 Jul 2019, In : Bioinformatics. 35, 14, p. i548-i557 btz315.

    Research output: Contribution to journalArticleScientificpeer-review

  3. Published

    Harmonizable mixture kernels with variational Fourier features

    Shen, Z., Heinonen, M. & Kaski, S., May 2019, The 22nd International Conference on Artificial Intelligence and Statistic. p. 1812-1821 (Proceedings of Machine Learning Research; vol. 89).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  4. Published

    Deep learning with differential Gaussian process flows

    Hegde, P., Heinonen, M., Lähdesmäki, H. & Kaski, S., Apr 2019, The 22nd International Conference on Artificial Intelligence and Statistic. Vol. 89. p. 1-15 16 p. (Proceedings of Machine Learning Research; vol. 89).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  5. Accepted/In press

    Deep convolutional Gaussian process

    Blomqvist, K., Kaski, S. & Heinonen, M., 2019, (Accepted/In press) Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  6. Accepted/In press

    ODE2VAE: Deep generative second order ODEs with Bayesian neural networks

    Yildiz, C., Heinonen, M. & Lähdesmäki, H., 2019, (Accepted/In press) NeurIPS.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  7. 2018
  8. Published

    Temporal clustering analysis of endothelial cell gene expression following exposure to a conventional radiotherapy dose fraction using Gaussian process clustering

    Heinonen, M., Milliat, F., Benadjaoud, M. A., François, A., Buard, V., Tarlet, G., D'Alché-Buc, F. & Guipaud, O., 1 Oct 2018, In : PloS one. 13, 10, p. 1-31 e0204960.

    Research output: Contribution to journalArticleScientificpeer-review

  9. Published

    Learning with multiple pairwise kernels for drug bioactivity prediction

    Cichonska, A., Pahikkala, T., Szedmak, S., Julkunen, H., Airola, A., Heinonen, M., Aittokallio, T. & Rousu, J., 1 Jul 2018, In : Bioinformatics. 34, 13, p. i509-i518

    Research output: Contribution to journalArticleScientificpeer-review

  10. Published

    MGPfusion: Predicting protein stability changes with Gaussian process kernel learning and data fusion

    Jokinen, E., Heinonen, M. & Lähdesmäki, H., 1 Jul 2018, In : Bioinformatics. 34, 13, p. i274-i283

    Research output: Contribution to journalArticleScientificpeer-review

  11. Published

    Flex ddG: Rosetta Ensemble-Based Estimation of Changes in Protein–Protein Binding Affinity upon Mutation

    Barlow, K., Conchuir, S., Thompson, S., Suresh, P., Lucas, J., Heinonen, M. & Kortemme, T., 31 May 2018, In : Journal of Physical Chemistry B. 122, 21, p. 5389-5399 11 p.

    Research output: Contribution to journalArticleScientificpeer-review

  12. Published

    A Nonparametric Spatio-temporal SDE Model

    Yildiz, C., Heinonen, M. & Lähdesmäki, H., 2018, NIPS 2018 Spatiotemporal Workshop: 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada. Neural Information Processing Systems Foundation, p. 1-5

    Research output: Chapter in Book/Report/Conference proceedingConference contributionProfessional

  13. Published

    Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching

    Yildiz, C., Heinonen, M., Intosalmi, J., Mannerström, H. & Lähdesmäki, H., 2018, IEEE International Workshop on Machine Learning for Signal Processing. IEEE, 6 p. 8516991

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  14. Published

    Learning unknown ODE models with Gaussian processes

    Heinonen, M., Yildiz, C., Mannerström, H., Intosalmi, J. & Lähdesmäki, H., 2018, Proceedings of the 35th International Conference on Machine Learning, ICML 2018. Vol. 5. p. 3120-3132 13 p. (Proceedings of Machine Learning Research; vol. 80).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  15. Published

    Variational zero-inflated Gaussian processes with sparse kernels

    Hegde, P., Heinonen, M. & Kaski, S., 2018, 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018. AUAI Press, Vol. 1. p. 361-371 148

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  16. 2017
  17. Published

    A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings

    Remes, S., Heinonen, M. & Kaski, S., Nov 2017, Proceedings of the 9th Asian Conference on Machine Learning. Zhang, M-L. & Noh, Y-K. (eds.). p. 455-470 16 p. (Proceedings of Machine Learning Research; vol. 77).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  18. Published

    Non-Stationary Spectral Kernels

    Remes, S., Heinonen, M. & Kaski, S., 2017, Advances in Neural Information Processing Systems 30: Proceedings of NIPS2017. Curran Associates, Inc., p. 4645-4654 (Advances in Neural Information Processing Systems; vol. 30).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  19. 2016
  20. Published

    Genome wide analysis of protein production load in Trichoderma reesei

    Pakula, T. M., Nygren, H., Barth, D., Heinonen, M., Castillo, S., Penttilä, M. & Arvas, M., 28 Jun 2016, In : Biotechnology for Biofuels. 9, 1, p. 1-26 132.

    Research output: Contribution to journalArticleScientificpeer-review

  21. Published

    Generating data to improve protein stability prediction

    Heinonen, M., Jokinen, E., Kaski, S., Rousu, J. & Lähdesmäki, H., 2016, Patent No. Provisional US 62/361511

    Research output: PatentScientific

  22. Published

    Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo

    Heinonen, M., Mannerström, H., Rousu, J., Kaski, S. & Lähdesmäki, H., 2016, Proceedings of the 19th International Conference on Artificial Intelligence and Statistics: JMLR: W&CP. JMLR, p. 732-740 9 p. ( JMLR: Workshop and Conference Proceedings; vol. 51).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  23. Published

    Random Fourier Features For Operator-Valued Kernels

    Brault, R., Heinonen, M. & d'Alché-Buc, F., 2016, Proceedings of the 8th Asian Conference on Machine Learning. Durrant, B. & Kim, K-E. (eds.). p. 110-125 (Proceedings of Machine Learning Research; vol. 63).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  24. 2015
  25. Published

    Detecting time periods of differential gene expression using Gaussian processes: an application to endothelial cells exposed to radiotherapy dose fraction

    Heinonen, M., Guipaud, O., Milliat, F., Buard, V., Micheau, B., Tarlet, G., Benderitter, M., Zehraoui, F. & d'Alche-Buc, F., 1 Mar 2015, In : Bioinformatics. 31, 5, p. 728-735 8 p.

    Research output: Contribution to journalArticleScientificpeer-review

  26. 2014
  27. Published

    Learning nonparametric differential equations with operator-valued kernels and gradient matching

    Heinonen, M. & d'Alché-Buc, F., 2014.

    Research output: Working paperProfessional

  28. 2013
  29. Published

    Computational methods for small molecules

    Heinonen, M., 2013, University of Helsinki. 109 p.

    Research output: ThesisDoctoral ThesisCollection of Articles

  30. Published

    Metabolite Identification through Machine Learning- Tackling CASMI Challenge Using FingerID

    Shen, H., Zamboni, N., Heinonen, M. & Rousu, J., 2013, In : METABOLITES. 3, 2, p. 484-505

    Research output: Contribution to journalArticleScientificpeer-review

  31. 2012
  32. Published

    Efficient Path Kernels for Reaction Function Prediction

    Heinonen, M., Välimäki, N., Mäkinen, V. & Rousu, J., 2012, Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms. p. 202-207

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  33. Published

    Metabolite identification and molecular fingerprint prediction through machine learning

    Heinonen, M., Shen, H., Zamboni, N. & Rousu, J., 2012, In : Bioinformatics. 28, 18, p. 2333-2341

    Research output: Contribution to journalArticleScientificpeer-review

  34. 2011
  35. Published

    Computing Atom Mappings for Biochemical Reactions without Subgraph Isomorphism

    Heinonen, M., Lappalainen, S., Mielikäinen, T. & Rousu, J., Jan 2011, In : JOURNAL OF COMPUTATIONAL BIOLOGY. 18, 1, p. 43-58 16 p.

    Research output: Contribution to journalArticleScientificpeer-review

  36. 2010
  37. Published

    Multilabel classification of drug-like molecules via max-margin conditional random fields

    Su, H., Heinonen, M. & Rousu, J., 2010, Proceedings of The Fifth European Workshop on Probabilistic Graphical Models (PGM-2010): 13-15 September, 2010, Helsinki, Finland. Myllymäki, P., Roos, T. & Jaakkola, T. (eds.). HIIT, p. 265-272 (HIIT Publications; vol. 2010, no. 2).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  38. Published

    Structured output prediction of anti-cancer drug activity

    Su, H., Heinonen, M. & Rousu, J., 2010, Pattern Recognition in Bioinformatics: 5th IAPR International Conference, PRIB 2010, Nijmegen, The Netherlands, September 22-24, 2010. Proceedings. Dijkstra, T. M. H., Tsivtsivadze, E., Marchiori, E. & Heskes, T. (eds.). Springer-Verlag, p. 38-49 ( Lecture Notes in Computer Science; vol. 6282).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  39. 2008
  40. Published

    FiD: a software for ab initio structural identification of product ions from tandem mass spectrometric data

    Heinonen, M., Rantanen, A., Mielikaeinen, T., Kokkonen, J., Kiuru, J., Ketola, R. A. & Rousu, J., Oct 2008, In : Rapid Communications in Mass Spectrometry. 22, 19, p. 3043-3052 10 p.

    Research output: Contribution to journalArticleScientificpeer-review

  41. 2006
  42. Published

    Ab Initio prediction of molecular fragments from tandem mass spectrometry data

    Heinonen, M., Rantanen, A., Mielikäinen, T., Pitkänen, E., Kokkonen, J. T. & Rousu, J., 2006, Proceedings of the German Conference on Bioinformatics. Huson, D., Kohlbacher, O., Lupas, A., Nieselt, K. & Zell, A. (eds.). Gesellschaft für Informatik (GI), p. 40-53 (Lecture Notes in Informatics (LNI); vol. P83).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

ID: 52283