Jorma Boberg

979 total citations
22 papers, 630 citations indexed

About

Jorma Boberg is a scholar working on Molecular Biology, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Jorma Boberg has authored 22 papers receiving a total of 630 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 10 papers in Artificial Intelligence and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Jorma Boberg's work include Natural Language Processing Techniques (7 papers), Machine Learning in Bioinformatics (6 papers) and Biomedical Text Mining and Ontologies (6 papers). Jorma Boberg is often cited by papers focused on Natural Language Processing Techniques (7 papers), Machine Learning in Bioinformatics (6 papers) and Biomedical Text Mining and Ontologies (6 papers). Jorma Boberg collaborates with scholars based in Finland, Netherlands and Sweden. Jorma Boberg's co-authors include Tapio Salakoski, Jouni Järvinen, Filip Ginter, Sampo Pyysalo, Juho Heimonen, Jari Björne, Tapio Pahikkala, Mauno Vihinen, Antti Airola and Evgeni Tsivtsivadze and has published in prestigious journals such as Bioinformatics, IEEE Transactions on Biomedical Engineering and BMC Bioinformatics.

In The Last Decade

Jorma Boberg

21 papers receiving 582 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jorma Boberg Finland 11 439 429 63 29 28 22 630
Jun Huan United States 11 182 0.4× 147 0.3× 143 2.3× 33 1.1× 10 0.4× 29 411
J Tsujii Japan 7 599 1.4× 805 1.9× 11 0.2× 39 1.3× 58 2.1× 20 928
Yuk Wah Wong United States 8 292 0.7× 752 1.8× 114 1.8× 27 0.9× 11 0.4× 11 856
Daniel Ting United States 9 121 0.3× 116 0.3× 25 0.4× 18 0.6× 15 0.5× 19 313
Chew-Lim Tan Singapore 11 311 0.7× 651 1.5× 133 2.1× 20 0.7× 45 1.6× 17 805
Tom Christiansen United States 7 113 0.3× 150 0.3× 9 0.1× 28 1.0× 11 0.4× 13 330
Gerson Zaverucha Brazil 10 80 0.2× 237 0.6× 30 0.5× 43 1.5× 18 0.6× 49 403
Liang Kong China 11 348 0.8× 193 0.4× 52 0.8× 30 1.0× 10 0.4× 33 598
Akihiro Tamura Japan 12 378 0.9× 633 1.5× 161 2.6× 13 0.4× 8 0.3× 50 773

Countries citing papers authored by Jorma Boberg

Since Specialization
Citations

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

Fields of papers citing papers by Jorma Boberg

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jorma Boberg

This figure shows the co-authorship network connecting the top 25 collaborators of Jorma Boberg. A scholar is included among the top collaborators of Jorma Boberg 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 Jorma Boberg. Jorma Boberg 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.
Pahikkala, Tapio, Hanna Suominen, & Jorma Boberg. (2012). Efficient cross-validation for kernelized least-squares regression with sparse basis expansions. Machine Learning. 87(3). 381–407. 11 indexed citations
2.
Tsivtsivadze, Evgeni, et al.. (2010). Efficient remote homology detection. Munich Personal RePEc Archive (Ludwig Maximilian University of Munich). 121(8). 117–20. 2 indexed citations
3.
Airola, Antti, Tapio Pahikkala, Jorma Boberg, & Tapio Salakoski. (2010). Applying Permutation Tests for Assessing the Statistical Significance of Wrapper Based Feature Selection. 8. 989–994.
4.
Pahikkala, Tapio, Antti Airola, Hanna Suominen, Jorma Boberg, & Tapio Salakoski. (2008). Efficient AUC Maximization with Regularized Least-Squares. 12–19. 7 indexed citations
5.
Tsivtsivadze, Evgeni, Tapio Pahikkala, Antti Airola, Jorma Boberg, & Tapio Salakoski. (2008). A Sparse Regularized Least-Squares Preference Learning Algorithm. 76–83. 7 indexed citations
6.
Pahikkala, Tapio, Sampo Pyysalo, Jorma Boberg, Jouni Järvinen, & Tapio Salakoski. (2008). Matrix representations, linear transformations, and kernels for disambiguation in natural language. Machine Learning. 74(2). 133–158. 10 indexed citations
7.
Pyysalo, Sampo, Filip Ginter, Juho Heimonen, et al.. (2007). BioInfer: a corpus for information extraction in the biomedical domain. BMC Bioinformatics. 8(1). 50–50. 337 indexed citations
8.
Pahikkala, Tapio, Jorma Boberg, & Tapio Salakoski. (2006). Fast n-Fold Cross-Validation for Regularized Least-Squares. 20 indexed citations
9.
Pahikkala, Tapio, Sampo Pyysalo, Filip Ginter, et al.. (2005). Kernels Incorporating Word Positional Information in Natural Language Disambiguation Tasks.. The Florida AI Research Society. 442–448. 9 indexed citations
10.
Pyysalo, Sampo, Filip Ginter, Tapio Pahikkala, et al.. (2005). Evaluation of two dependency parsers on biomedical corpus targeted at protein–protein interactions. International Journal of Medical Informatics. 75(6). 430–442. 17 indexed citations
11.
Pahikkala, Tapio, Filip Ginter, Jorma Boberg, Jouni Järvinen, & Tapio Salakoski. (2005). Contextual weighting for Support Vector Machines in literature mining: an application to gene versus protein name disambiguation. BMC Bioinformatics. 6(1). 157–157. 14 indexed citations
12.
Pahikkala, Tapio, et al.. (2005). IMPROVING THE PERFORMANCE OF BAYESIAN AND SUPPORT VECTOR CLASSIFIERS IN WORD SENSE DISAMBIGUATION USING POSITIONAL INFORMATION. 7 indexed citations
13.
Ginter, Filip, Jorma Boberg, Jouni Järvinen, & Tapio Salakoski. (2004). New Techniques for Disambiguation in Natural Language and Their Application to Biological Text. Journal of Machine Learning Research. 5. 605–621. 34 indexed citations
14.
Pyysalo, Sampo, Filip Ginter, Tapio Pahikkala, et al.. (2004). Analysis of link grammar on biomedical dependency corpus targeted at protein-protein interactions. 15–15. 16 indexed citations
15.
Riikonen, Pentti, Jorma Boberg, T. Salakoski, & Mauno Vihinen. (2002). Mobile access to biological databases on the Internet. IEEE Transactions on Biomedical Engineering. 49(12). 1477–1479. 9 indexed citations
16.
Riikonen, Pentti, Jorma Boberg, Tapio Salakoski, & Mauno Vihinen. (2001). BioWAP, mobile Internet service for bioinformatics. Bioinformatics. 17(9). 855–856. 4 indexed citations
17.
Boberg, Jorma & Tapio Salakoski. (1997). Representative noise-free complete-link classification with application to protein structures. Pattern Recognition. 30(3). 467–482. 1 indexed citations
18.
Boberg, Jorma, Tapio Salakoski, & Mauno Vihinen. (1995). Accurate prediction of protein secondary structural class with fuzzy structural vectors. Protein Engineering Design and Selection. 8(6). 505–512. 13 indexed citations
19.
Boberg, Jorma & Tapio Salakoski. (1993). General formulation and evaluation of agglomerative clustering methods with metric and non-metric distances. Pattern Recognition. 26(9). 1395–1406. 22 indexed citations
20.
Boberg, Jorma, Tapio Salakoski, & Mauno Vihinen. (1992). Selection of a representative set of structures from brookhaven protein data bank. Proteins Structure Function and Bioinformatics. 14(2). 265–276. 48 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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