Johan Pensar

1.4k total citations
40 papers, 563 citations indexed

About

Johan Pensar is a scholar working on Artificial Intelligence, Molecular Biology and Statistics and Probability. According to data from OpenAlex, Johan Pensar has authored 40 papers receiving a total of 563 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 13 papers in Molecular Biology and 10 papers in Statistics and Probability. Recurrent topics in Johan Pensar's work include Bayesian Modeling and Causal Inference (16 papers), Genomics and Phylogenetic Studies (5 papers) and Bayesian Methods and Mixture Models (4 papers). Johan Pensar is often cited by papers focused on Bayesian Modeling and Causal Inference (16 papers), Genomics and Phylogenetic Studies (5 papers) and Bayesian Methods and Mixture Models (4 papers). Johan Pensar collaborates with scholars based in Finland, Norway and United Kingdom. Johan Pensar's co-authors include Jukka Corander, Santeri Puranen, Maiju Pesonen, Timo Koski, Ida Scheel, Dag Einar Sommervoll, Anders Hjort, Anita C. Schürch, Janetta Top and Sergio Arredondo-Alonso and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Johan Pensar

36 papers receiving 553 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Johan Pensar Finland 13 154 110 102 67 64 40 563
Sandeep Kaur India 17 197 1.3× 41 0.4× 84 0.8× 130 1.9× 25 0.4× 88 989
Mohammad Javad Hosseini Iran 15 79 0.5× 113 1.0× 158 1.5× 74 1.1× 52 0.8× 48 765
Andrew E. Waters United States 11 225 1.5× 163 1.5× 200 2.0× 165 2.5× 43 0.7× 27 927
Yijun Ding China 12 95 0.6× 114 1.0× 23 0.2× 32 0.5× 82 1.3× 20 685
Anne Spencer Ross United States 11 147 1.0× 94 0.9× 36 0.4× 44 0.7× 96 1.5× 19 705
Theodore Kypraios United Kingdom 17 107 0.7× 117 1.1× 199 2.0× 24 0.4× 50 0.8× 61 908
Probodh Borah India 19 222 1.4× 15 0.1× 204 2.0× 142 2.1× 28 0.4× 110 1.1k
Karel Břinda United States 9 206 1.3× 42 0.4× 47 0.5× 29 0.4× 177 2.8× 19 469
Jiří Schindler United States 21 168 1.1× 181 1.6× 121 1.2× 36 0.5× 56 0.9× 72 1.8k
Zhenpeng Li China 13 125 0.8× 19 0.2× 109 1.1× 61 0.9× 106 1.7× 41 518

Countries citing papers authored by Johan Pensar

Since Specialization
Citations

This map shows the geographic impact of Johan Pensar'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 Johan Pensar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Johan Pensar more than expected).

Fields of papers citing papers by Johan Pensar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Johan Pensar. 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 Johan Pensar. The network helps show where Johan Pensar may publish in the future.

Co-authorship network of co-authors of Johan Pensar

This figure shows the co-authorship network connecting the top 25 collaborators of Johan Pensar. A scholar is included among the top collaborators of Johan Pensar 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 Johan Pensar. Johan Pensar 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.
Pensar, Johan, et al.. (2025). Recovery and inference of causal effects with sequential adjustment for confounding and attrition. SHILAP Revista de lepidopterología. 13(1).
2.
Pavlović, Milena, Chakravarthi Kanduri, Johan Pensar, et al.. (2024). Improving generalization of machine learning-identified biomarkers using causal modelling with examples from immune receptor diagnostics. Nature Machine Intelligence. 6(1). 15–24. 10 indexed citations
3.
Horsfield, Samuel, Anna K. Pöntinen, Sergio Arredondo-Alonso, et al.. (2024). Pangenome-spanning epistasis and coselection analysis via de Bruijn graphs. Genome Research. 34(7). 1081–1088.
4.
5.
Corander, Jukka, William P. Hanage, & Johan Pensar. (2022). Causal discovery for the microbiome. The Lancet Microbe. 3(11). e881–e887. 20 indexed citations
6.
Hjort, Anders, Johan Pensar, Ida Scheel, & Dag Einar Sommervoll. (2022). House price prediction with gradient boosted trees under different loss functions. Journal of Property Research. 39(4). 338–364. 27 indexed citations
7.
Mageiros, Leonardos, Guillaume Méric, Sion Bayliss, et al.. (2021). Author Correction: Genome evolution and the emergence of pathogenicity in avian Escherichia coli. Nature Communications. 12(1). 1934–1934. 6 indexed citations
8.
Mageiros, Leonardos, Guillaume Méric, Sion Bayliss, et al.. (2021). Genome evolution and the emergence of pathogenicity in avian Escherichia coli. Nature Communications. 12(1). 765–765. 83 indexed citations
9.
Top, Janetta, Sergio Arredondo-Alonso, Anita C. Schürch, et al.. (2020). Genomic rearrangements uncovered by genome-wide co-evolution analysis of a major nosocomial pathogen, Enterococcus faecium. Microbial Genomics. 6(12). 11 indexed citations
10.
Pensar, Johan, Santeri Puranen, Brian J. Arnold, et al.. (2019). Genome-wide epistasis and co-selection study using mutual information. Nucleic Acids Research. 47(18). e112–e112. 30 indexed citations
11.
Pensar, Johan, et al.. (2019). High-dimensional structure learning of binary pairwise Markov networks: A comparative numerical study. Computational Statistics & Data Analysis. 141. 62–76. 4 indexed citations
12.
Hyttinen, Antti, Johan Pensar, Juha Kontinen, & Jukka Corander. (2018). Structure Learning for Bayesian Networks over Labeled DAGs. Työväentutkimus Vuosikirja. 133–144. 2 indexed citations
13.
Puranen, Santeri, Maiju Pesonen, Johan Pensar, et al.. (2018). SuperDCA for genome-wide epistasis analysis. Microbial Genomics. 4(6). 17 indexed citations
14.
Pensar, Johan, et al.. (2017). Learning Gaussian graphical models with fractional marginal pseudo-likelihood. International Journal of Approximate Reasoning. 83. 21–42. 9 indexed citations
15.
Pensar, Johan, et al.. (2016). Marginal Pseudo-Likelihood Learning of Discrete Markov Network Structures. Bayesian Analysis. 12(4). 13 indexed citations
16.
Pensar, Johan, et al.. (2015). The role of local partial independence in learning of Bayesian networks. International Journal of Approximate Reasoning. 69. 91–105. 19 indexed citations
17.
Janhunen, Tomi, et al.. (2015). Learning discrete decomposable graphical models via constraint optimization. Statistics and Computing. 27(1). 115–130. 5 indexed citations
18.
Xiong, Jie, et al.. (2015). Marginal and simultaneous predictive classification using stratified graphical models. Advances in Data Analysis and Classification. 10(3). 305–326. 3 indexed citations
19.
Pensar, Johan, et al.. (2014). Labeled directed acyclic graphs: a generalization of context-specific independence in directed graphical models. Data Mining and Knowledge Discovery. 29(2). 503–533. 25 indexed citations
20.
Johansson, A, Patrick Jern, Pekka Santtila, et al.. (2012). The Genetics of Sexuality and Aggression (GSA) Twin Samples in Finland. Twin Research and Human Genetics. 16(1). 150–156. 56 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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