Jörg Hakenberg

4.0k total citations
40 papers, 1.7k citations indexed

About

Jörg Hakenberg is a scholar working on Molecular Biology, Artificial Intelligence and Genetics. According to data from OpenAlex, Jörg Hakenberg has authored 40 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Molecular Biology, 21 papers in Artificial Intelligence and 9 papers in Genetics. Recurrent topics in Jörg Hakenberg's work include Biomedical Text Mining and Ontologies (29 papers), Semantic Web and Ontologies (16 papers) and Bioinformatics and Genomic Networks (12 papers). Jörg Hakenberg is often cited by papers focused on Biomedical Text Mining and Ontologies (29 papers), Semantic Web and Ontologies (16 papers) and Bioinformatics and Genomic Networks (12 papers). Jörg Hakenberg collaborates with scholars based in United States, Germany and United Kingdom. Jörg Hakenberg's co-authors include Ulf Leser, Conrad Plake, Graciela Gonzalez‐Hernandez, Michael Schroeder, Robert Leaman, Philippe Thomas, Domonkos Tikk, Luis Tari, Chitta Baral and Rong Chen and has published in prestigious journals such as Nucleic Acids Research, Nature Genetics and Bioinformatics.

In The Last Decade

Jörg Hakenberg

40 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jörg Hakenberg United States 22 1.4k 709 288 128 61 40 1.7k
Simon Jupp United Kingdom 15 604 0.4× 219 0.3× 342 1.2× 45 0.4× 36 0.6× 45 1.0k
Mohamed Abouelhoda Saudi Arabia 20 822 0.6× 335 0.5× 388 1.3× 34 0.3× 170 2.8× 72 1.6k
W. Jim Zheng United States 21 624 0.4× 315 0.4× 103 0.4× 95 0.7× 53 0.9× 75 1.2k
Victor Felix United States 10 1.4k 1.0× 362 0.5× 245 0.9× 181 1.4× 41 0.7× 14 1.7k
Misha Kapushesky United Kingdom 17 1.4k 1.0× 142 0.2× 219 0.8× 93 0.7× 58 1.0× 24 1.8k
Michael M. Hoffman Canada 17 1.5k 1.1× 169 0.2× 311 1.1× 76 0.6× 51 0.8× 37 2.0k
Kyubum Lee United States 16 612 0.4× 257 0.4× 120 0.4× 122 1.0× 71 1.2× 32 1.1k
Andra Waagmeester Netherlands 13 862 0.6× 127 0.2× 109 0.4× 153 1.2× 72 1.2× 30 1.2k
Janos X. Binder Germany 3 1.0k 0.7× 159 0.2× 164 0.6× 153 1.2× 51 0.8× 3 1.3k
Sven Laur Estonia 11 509 0.4× 310 0.4× 70 0.2× 64 0.5× 76 1.2× 27 1.1k

Countries citing papers authored by Jörg Hakenberg

Since Specialization
Citations

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

Fields of papers citing papers by Jörg Hakenberg

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jörg Hakenberg

This figure shows the co-authorship network connecting the top 25 collaborators of Jörg Hakenberg. A scholar is included among the top collaborators of Jörg Hakenberg 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 Jörg Hakenberg. Jörg Hakenberg 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.
Sundaram, Laksshman, Hong Gao, Samskruthi Reddy Padigepati, et al.. (2018). Predicting the clinical impact of human mutation with deep neural networks. Nature Genetics. 50(8). 1161–1170. 263 indexed citations
2.
Thomas, Philippe, et al.. (2016). SETH detects and normalizes genetic variants in text. Bioinformatics. 32(18). 2883–2885. 27 indexed citations
3.
Hakenberg, Jörg, Wei‐Yi Cheng, Philippe Thomas, et al.. (2016). Integrating 400 million variants from 80,000 human samples with extensive annotations: towards a knowledge base to analyze disease cohorts. BMC Bioinformatics. 17(1). 24–24. 7 indexed citations
4.
Chen, Brenden, Jörg Hakenberg, Wanqiong Qiao, et al.. (2016). Acute Intermittent Porphyria: Predicted Pathogenicity ofHMBSVariants Indicates Extremely Low Penetrance of the Autosomal Dominant Disease. Human Mutation. 37(11). 1215–1222. 126 indexed citations
5.
Ma, Meng, Ru Ying, Ling-Shiang Chuang, et al.. (2015). Disease-associated variants in different categories of disease located in distinct regulatory elements. BMC Genomics. 16(S8). S3–S3. 40 indexed citations
6.
Hakenberg, Jörg, D. A. Voronov, Shanshan Liang, et al.. (2012). A SNPshot of PubMed to associate genetic variants with drugs, diseases, and adverse reactions. Journal of Biomedical Informatics. 45(5). 842–850. 35 indexed citations
7.
Hakenberg, Jörg, Martin Gerner, Maximilian Haeussler, et al.. (2011). The GNAT library for local and remote gene mention normalization. Bioinformatics. 27(19). 2769–2771. 54 indexed citations
8.
Hakenberg, Jörg, Illés Solt, Domonkos Tikk, et al.. (2011). MOLECULAR EVENT EXTRACTION FROM LINK GRAMMAR PARSE TREES IN THE BIONLP’09 SHARED TASK. Computational Intelligence. 27(4). 665–680. 3 indexed citations
9.
Hakenberg, Jörg, Robert Leaman, Nguyen Vo, et al.. (2010). Efficient Extraction of Protein-Protein Interactions from Full-Text Articles. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 7(3). 481–494. 30 indexed citations
10.
Tikk, Domonkos, et al.. (2010). A Comprehensive Benchmark of Kernel Methods to Extract Protein–Protein Interactions from Literature. PLoS Computational Biology. 6(7). e1000837–e1000837. 133 indexed citations
11.
12.
Tari, Luis, Jörg Hakenberg, Yi Chen, et al.. (2010). Incremental Information Extraction Using Relational Databases. IEEE Transactions on Knowledge and Data Engineering. 24(1). 86–99. 27 indexed citations
13.
Nguyen, Long, et al.. (2009). High-performance information extraction with AliBaba. 1140–1143. 18 indexed citations
14.
Plake, Conrad, Löıc A. Royer, Rainer Winnenburg, Jörg Hakenberg, & Michael Schroeder. (2009). GoGene: gene annotation in the fast lane. Nucleic Acids Research. 37(Web Server). W300–W304. 28 indexed citations
15.
Morgan, Alexander A., Zhiyong Lu, Xinglong Wang, et al.. (2008). Overview of BioCreative II gene normalization. Genome biology. 9(S2). S3–S3. 241 indexed citations
16.
Tari, Luis, Jörg Hakenberg, Graciela Gonzalez‐Hernandez, & Chitta Baral. (2008). QUERYING PARSE TREE DATABASE OF MEDLINE TEXT TO SYNTHESIZE USER-SPECIFIC BIOMOLECULAR NETWORKS. PubMed. 87–98. 14 indexed citations
17.
Hakenberg, Jörg, Steffen Bickel, Conrad Plake, et al.. (2005). Systematic feature evaluation for gene name recognition. BMC Bioinformatics. 6(S1). S9–S9. 33 indexed citations
18.
Leser, Ulf & Jörg Hakenberg. (2005). What makes a gene name? Named entity recognition in the biomedical literature. Briefings in Bioinformatics. 6(4). 357–369. 100 indexed citations
19.
Bickel, Steffen, Ulf Brefeld, Lukas C. Faulstich, et al.. (2004). A Support Vector Machine classifier for gene name recognition. 1 indexed citations
20.
Hakenberg, Jörg, Sebastian Schmeier, Axel Kowald, Edda Klipp, & Ulf Leser. (2004). Finding Kinetic Parameters Using Text Mining. OMICS A Journal of Integrative Biology. 8(2). 131–152. 26 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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