Markus Heinonen

1.5k total citations
33 papers, 666 citations indexed

About

Markus Heinonen is a scholar working on Molecular Biology, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Markus Heinonen has authored 33 papers receiving a total of 666 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 13 papers in Artificial Intelligence and 6 papers in Computational Theory and Mathematics. Recurrent topics in Markus Heinonen's work include Gaussian Processes and Bayesian Inference (9 papers), Computational Drug Discovery Methods (6 papers) and T-cell and B-cell Immunology (4 papers). Markus Heinonen is often cited by papers focused on Gaussian Processes and Bayesian Inference (9 papers), Computational Drug Discovery Methods (6 papers) and T-cell and B-cell Immunology (4 papers). Markus Heinonen collaborates with scholars based in Finland, United States and Sweden. Markus Heinonen's co-authors include Juho Rousu, Huibin Shen, Nicola Zamboni, Harri Lähdesmäki, James E. Lucas, Pooja Suresh, Shane Ó Conchúir, Kyle A. Barlow, Tanja Kortemme and Samuel Thompson and has published in prestigious journals such as Blood, Bioinformatics and PLoS ONE.

In The Last Decade

Markus Heinonen

31 papers receiving 655 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Markus Heinonen Finland 13 485 140 95 87 78 33 666
Jianbo Fu China 17 806 1.7× 223 1.6× 113 1.2× 76 0.9× 53 0.7× 28 1.2k
Minjie Mou China 14 664 1.4× 284 2.0× 66 0.7× 49 0.6× 40 0.5× 30 941
Jiajun Hong China 12 614 1.3× 184 1.3× 86 0.9× 26 0.3× 35 0.4× 21 902
Zhaorong Li China 12 430 0.9× 171 1.2× 37 0.4× 32 0.4× 36 0.5× 17 603
Duolin Wang United States 15 874 1.8× 89 0.6× 59 0.6× 35 0.4× 33 0.4× 43 1.2k
Thamani Dahoun Switzerland 6 323 0.7× 256 1.8× 27 0.3× 33 0.4× 50 0.6× 6 565
Eric Stahlberg United States 18 546 1.1× 111 0.8× 43 0.5× 28 0.3× 22 0.3× 35 1.0k
K. Srinivas India 10 428 0.9× 101 0.7× 27 0.3× 38 0.4× 57 0.7× 26 793
Ziqi Pan China 13 263 0.5× 111 0.8× 42 0.4× 19 0.2× 47 0.6× 28 502
Len Trigg New Zealand 5 735 1.5× 100 0.7× 32 0.3× 24 0.3× 37 0.5× 8 1.1k

Countries citing papers authored by Markus Heinonen

Since Specialization
Citations

This map shows the geographic impact of Markus Heinonen's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Markus Heinonen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Markus Heinonen more than expected).

Fields of papers citing papers by Markus Heinonen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Markus Heinonen. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Markus Heinonen. The network helps show where Markus Heinonen may publish in the future.

Co-authorship network of co-authors of Markus Heinonen

This figure shows the co-authorship network connecting the top 25 collaborators of Markus Heinonen. A scholar is included among the top collaborators of Markus Heinonen based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Markus Heinonen. Markus Heinonen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Mesens, Natalie, et al.. (2025). VitroBert: modeling DILI by pretraining BERT on in vitro data. Journal of Cheminformatics. 17(1). 119–119.
2.
Heinonen, Markus, et al.. (2024). Probabilistic analysis of spatial viscoelastic cues in 3D cell culture using magnetic microrheometry. Biophysical Journal. 124(2). 351–362. 1 indexed citations
3.
Heinonen, Markus, Mikhail Kabeshov, Jon Paul Janet, et al.. (2024). Human-in-the-loop active learning for goal-oriented molecule generation. Journal of Cheminformatics. 16(1). 138–138. 7 indexed citations
4.
Heinonen, Markus, et al.. (2022). Modeling binding specificities of transcription factor pairs with random forests. BMC Bioinformatics. 23(1). 212–212. 5 indexed citations
5.
Voronov, Alexey, Kostas Papadopoulos, Esben Jannik Bjerrum, et al.. (2022). Human-in-the-loop assisted de novo molecular design. Journal of Cheminformatics. 14(1). 86–86. 14 indexed citations
6.
Heinonen, Markus, et al.. (2021). Continuous-time Model-based Reinforcement Learning. Aaltodoc (Aalto University). 1 indexed citations
7.
Jokinen, Emmi, Jani Huuhtanen, Satu Mustjoki, Markus Heinonen, & Harri Lähdesmäki. (2021). Predicting recognition between T cell receptors and epitopes with TCRGP. PLoS Computational Biology. 17(3). e1008814–e1008814. 83 indexed citations
8.
Voutilainen, Sanni, Markus Heinonen, Martina Andberg, et al.. (2020). Substrate specificity of 2-deoxy-D-ribose 5-phosphate aldolase (DERA) assessed by different protein engineering and machine learning methods. Applied Microbiology and Biotechnology. 104(24). 10515–10529. 23 indexed citations
9.
Heinonen, Markus, et al.. (2019). ODE2VAE: Deep generative second order ODEs with Bayesian neural networks. arXiv (Cornell University). 32. 13412–13421. 14 indexed citations
10.
Barlow, Kyle A., Shane Ó Conchúir, Samuel Thompson, et al.. (2018). Flex ddG: Rosetta Ensemble-Based Estimation of Changes in Protein–Protein Binding Affinity upon Mutation. The Journal of Physical Chemistry B. 122(21). 5389–5399. 158 indexed citations
11.
Heinonen, Markus, et al.. (2018). Variational zero-inflated Gaussian processes with sparse kernels. Uncertainty in Artificial Intelligence. 361–371. 1 indexed citations
12.
Heinonen, Markus, et al.. (2018). Learning unknown ODE models with Gaussian processes. Aaltodoc (Aalto University). 1959–1968. 3 indexed citations
13.
Heinonen, Markus, et al.. (2018). Harmonizable mixture kernels with variational Fourier features. arXiv (Cornell University). 3273–3282. 1 indexed citations
14.
Heinonen, Markus, et al.. (2017). Latent Correlation Gaussian Processes. arXiv (Cornell University). 1 indexed citations
15.
Heinonen, Markus, et al.. (2017). A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings. Aaltodoc (Aalto University). 455–470. 1 indexed citations
16.
Heinonen, Markus, et al.. (2017). Non-Stationary Spectral Kernels. arXiv (Cornell University). 30. 4645–4654. 18 indexed citations
17.
Heinonen, Markus, et al.. (2016). Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo. International Conference on Artificial Intelligence and Statistics. 732–740. 16 indexed citations
18.
Pakula, Tiina, Heli Nygrén, Dorothee Barth, et al.. (2016). Genome wide analysis of protein production load in Trichoderma reesei. Biotechnology for Biofuels. 9(1). 132–132. 18 indexed citations
19.
Heinonen, Markus, Olivier Guipaud, Fabien Milliat, et al.. (2014). Detecting time periods of differential gene expression using Gaussian processes: an application to endothelial cells exposed to radiotherapy dose fraction. Bioinformatics. 31(5). 728–735. 28 indexed citations
20.
Shen, Huibin, Nicola Zamboni, Markus Heinonen, & Juho Rousu. (2013). Metabolite Identification through Machine Learning— Tackling CASMI Challenge Using FingerID. Metabolites. 3(2). 484–505. 25 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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