Hagit Shatkay

3.7k citations
52 papers · 1.5k indexed · h-index 19
Topics
Biomedical Text Mining and Ontologies (38 papers)Bioinformatics and Genomic Networks (12 papers)Image Retrieval and Classification Techniques (9 papers)

In The Last Decade

Hagit Shatkay

50 papers receiving 1.4k citations

Peers

Hagit Shatkay
Comparison fields: 5 of 112
  • Molecular Biology 994
  • Artificial Intelligence 677
  • Computer Vision and Pattern Recognition 221
  • Aerospace Engineering 109
  • Computational Theory and Mathematics 94
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Countries citing papers authored by Hagit Shatkay

Since Specialization
Citations

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

Fields of papers citing papers by Hagit Shatkay

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hagit Shatkay

This figure shows the co-authorship network connecting the top 25 collaborators of Hagit Shatkay. A scholar is included among the top collaborators of Hagit Shatkay 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 Hagit Shatkay. Hagit Shatkay 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
#WorkIndexed citations
1 0
2 4
3 6
4
UDEL CIS Working Notes in ImageCLEF 2016.
4
5
CIS UDEL Working Notes on ImageCLEF 2015: Compound Figure Detection Task.
4
6 13
7 68
8 16
9 2
10 6
11 64
12 56
13 59
14 124
15
Applying Probabilistic Thematic Clustering for Classification in the TREC 2005 Genomics Track
8
16 31
17
Learning Geometrically-Constrained Hidden Markov Models for Robot Navigation: Bridging the Geometrical-Topological Gap
3
18 12
19
Learning Topological Maps from Weak Odometric Information
10
20
Learning topological maps with weak local odometric information
117

About Hagit Shatkay

Hagit Shatkay is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition, having authored 52 papers that have together received 1.5k indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (38 papers), Bioinformatics and Genomic Networks (12 papers) and Image Retrieval and Classification Techniques (9 papers). The work is most often cited by research in Artificial Intelligence (677 citations), Molecular Biology (994 citations) and Computer Vision and Pattern Recognition (221 citations). Hagit Shatkay has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include W. John Wilbur, Ronen Feldman, Leslie Pack Kaelbling, Andrey Rzhetsky, Scott T. Brady, Mark S. Boguski, Stephen W. Edwards, Dorothea Blostein, Andrew Wong and Oliver Kohlbacher. Their work appears in journals such as Bioinformatics, Genome biology and BMC Bioinformatics.

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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