Henry Joutsijoki

532 total citations
40 papers, 367 citations indexed

About

Henry Joutsijoki is a scholar working on Molecular Biology, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Henry Joutsijoki has authored 40 papers receiving a total of 367 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 12 papers in Artificial Intelligence and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in Henry Joutsijoki's work include Pluripotent Stem Cells Research (8 papers), Identification and Quantification in Food (6 papers) and Neuroscience and Neural Engineering (6 papers). Henry Joutsijoki is often cited by papers focused on Pluripotent Stem Cells Research (8 papers), Identification and Quantification in Food (6 papers) and Neuroscience and Neural Engineering (6 papers). Henry Joutsijoki collaborates with scholars based in Finland, Estonia and United States. Henry Joutsijoki's co-authors include Martti Juhola, Katriina Aalto‐Setälä, Kirsi Penttinen, Jyrki Rasku, Jorma Laurikkala, Serkan Kıranyaz, Harri Siirtola, Kristian Meissner, Jari Hyttinen and Moncef Gabbouj and has published in prestigious journals such as Scientific Reports, BioMed Research International and Annals of Biomedical Engineering.

In The Last Decade

Henry Joutsijoki

38 papers receiving 356 citations

Peers

Henry Joutsijoki

CCM

CMN

Suiren Wan China

Jie Ni China

Jinhyoung Park South Korea

Linh T. Duong Vietnam

Florence Rossant France

Jameel Ahmed Pakistan

László Szabó Hungary

Drew Linsley United States

Steffen Oeltze‐Jafra Germany

Yuan Yang China

Suiren Wan China

Henry Joutsijoki

75 ×0.5

159 ×1.7

97 ×1.5

CCM

72 ×1.2

44 ×1.0

CMN

Citations per year, relative to Henry Joutsijoki Henry Joutsijoki (= 1×) peers Suiren Wan

Countries citing papers authored by Henry Joutsijoki

Since Specialization

Citations

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

Fields of papers citing papers by Henry Joutsijoki

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Henry Joutsijoki

This figure shows the co-authorship network connecting the top 25 collaborators of Henry Joutsijoki. A scholar is included among the top collaborators of Henry Joutsijoki 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 Henry Joutsijoki. Henry Joutsijoki is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Joutsijoki, Henry, et al.. (2023). Comparison of machine learning methods in the early identification of vasculitides, myositides and glomerulonephritides. Computer Methods and Programs in Biomedicine. 243. 107917–107917. 4 indexed citations

Joutsijoki, Henry, et al.. (2023). A Comparative Study of Machine Learning Algorithms and SIFT for Identifying the Quality of Human Induced Pluripotent Stem Cell Colony. Trepo - Institutional Repository of Tampere University. 1–7.

Juhola, Martti, Henry Joutsijoki, Kirsi Penttinen, et al.. (2022). Data analytics for cardiac diseases. Computers in Biology and Medicine. 142. 105218–105218. 11 indexed citations

Juhola, Martti, Henry Joutsijoki, Kirsi Penttinen, Disheet Shah, & Katriina Aalto‐Setälä. (2021). On computational classification of genetic cardiac diseases applying iPSC cardiomyocytes. Computer Methods and Programs in Biomedicine. 210. 106367–106367. 5 indexed citations

Juhola, Martti, Kirsi Penttinen, Henry Joutsijoki, & Katriina Aalto‐Setälä. (2020). Analysis of Drug Effects on iPSC Cardiomyocytes with Machine Learning. Annals of Biomedical Engineering. 49(1). 129–138. 33 indexed citations

Joutsijoki, Henry, et al.. (2019). Differentiation of genetic cardiac diseases on the basis of artificial intelligence. 15(3). 43–52. 2 indexed citations

Juhola, Martti, Henry Joutsijoki, Kirsi Penttinen, & Katriina Aalto‐Setälä. (2019). Machine learning to differentiate diseased cardiomyocytes from healthy control cells. Informatics in Medicine Unlocked. 14. 15–22. 8 indexed citations

Juhola, Martti, Henry Joutsijoki, Kirsi Penttinen, & Katriina Aalto‐Setälä. (2018). Detection of genetic cardiac diseases by Ca2+ transient profiles using machine learning methods. Scientific Reports. 8(1). 9355–9355. 42 indexed citations

Joutsijoki, Henry, et al.. (2017). Development of crime in England and Wales 1898–2001: Data mining using self-organising map. 1–8. 1 indexed citations

10.

Joutsijoki, Henry, et al.. (2017). GDP growth vs. criminal phenomena: data mining of Japan 1926–2013. AI & Society. 33(2). 261–274. 2 indexed citations

11.

Juhola, Martti, Kirsi Penttinen, Henry Joutsijoki, et al.. (2015). Signal analysis and classification methods for the calcium transient data of stem cell-derived cardiomyocytes. Computers in Biology and Medicine. 61. 1–7. 21 indexed citations

12.

Joutsijoki, Henry, et al.. (2015). On the influence of training data quality on text document classification using machine learning methods. 3(2). 143–143. 4 indexed citations

13.

Joutsijoki, Henry, Jyrki Rasku, Michelangelo Paci, et al.. (2014). Histogram-based classification of iPSC colony images using machine learning methods. 2611–2617. 7 indexed citations

14.

Joutsijoki, Henry. (2014). Half-Against-Half Structure with SVM and k-NN Classifiers in Benthic Macroinvertebrate Image Classification. Journal of Computers. 9(2). 5 indexed citations

15.

Joutsijoki, Henry, Kristian Meissner, Moncef Gabbouj, et al.. (2014). Evaluating the performance of artificial neural networks for the classification of freshwater benthic macroinvertebrates. Ecological Informatics. 20. 1–12. 26 indexed citations

16.

Joutsijoki, Henry. (2013). Half-against-half structure in classification of benthic macroinvertebrate images. PubMed. 2013. 3646–9. 4 indexed citations

17.

Joutsijoki, Henry, et al.. (2013). Machine learning approach to an otoneurological classification problem. PubMed. 6. 1294–1297. 6 indexed citations

18.

Joutsijoki, Henry. (2012). Variations on a Theme: The Classification of Benthic Macroinvertebrates. Trepo - Institutional Repository of Tampere University. 1 indexed citations

19.

Joutsijoki, Henry & Martti Juhola. (2011). Kernel selection in multi-class support vector machines and its consequence to the number of ties in majority voting method. Artificial Intelligence Review. 40(3). 213–230. 19 indexed citations

20.

Rasku, Jyrki, Henry Joutsijoki, Ilmari Pyykkö, & Martti Juhola. (2011). Prediction of a state of a subject on the basis of a stabilogram signal and video oculography test. Computer Methods and Programs in Biomedicine. 108(2). 580–588. 5 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.

Explore authors with similar magnitude of impact