Daniel Korn

21 papers receiving 401 citations

Daniel Korn's Hit Papers

STopTox: An in Silico Alternative to Animal Testing for Acute Systemic and Topical Toxicity 2022 · 135 citations
1350+1+2Years since publication4080120

Peers

Daniel Korn
Comparison fields: 5 of 104
  • Computational Theory and Mathematics 210
  • Infectious Diseases 68
  • Health Informatics 5
  • Small Animals 26
  • Chemical Health and Safety 2
Replace Mohan Rao with:
Mohan Rao United States
Raphael Taiwo Aruleba South Africa
Francis E. Agamah South Africa
Tiago Alves de Oliveira Brazil
Giulia Chemi Italy
Tayo Alex Adekiya South Africa
Shuaishi Gao China
Rishi R. Gupta United States
Arthur C. Silva Brazil
Emmanuel Israel Edache Nigeria
Daniel Korn relative to Mohan Rao United States Mohan Rao's profile →
Citations per field
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Citations per year

Countries citing papers authored by Daniel Korn

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Korn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Daniel Korn, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel Korn Line = papers co-authored together Daniel Korn links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.

#Work
1
STopTox: An in Silico Alternative to Animal Testing for Acute Systemic and Topical Toxicity
Hit paper breakdown →
2022135
2 202065
3 201846
4 202128
5 202028
6 202027
7 202015
8 202211
9 20238
10 20217
11 20226
12 20215
13
Cannibal: The History of the People-eaters
20015
14 20224
15 20233
16 20223
17 20213
18 20242
19 20252
20 20221

About Daniel Korn

Daniel Korn is a scholar working on Computational Theory and Mathematics, Molecular Biology, Artificial Intelligence, Infectious Diseases and Small Animals, having authored 26 papers that have together received 405 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (10 papers), Advanced Graph Neural Networks (3 papers), Genomics and Rare Diseases (3 papers), Bioinformatics and Genomic Networks (3 papers), Biomedical Text Mining and Ontologies (3 papers), Animal testing and alternatives (3 papers), SARS-CoV-2 and COVID-19 Research (2 papers) and Semantic Web and Ontologies (2 papers). The work is most often cited by research in Computational Theory and Mathematics (210 citations), Infectious Diseases (68 citations), Health Informatics (5 citations), Small Animals (26 citations) and Chemical Health and Safety (2 citations). Daniel Korn has collaborated with scholars based in United States, Brazil and Canada. Frequent co-authors include Alexander Tropsha, Eugene Muratov, Vinícius M. Alves, Tesia Bobrowski, Carolina Horta Andrade, Rodolpho C. Braga, Cleber C. Melo‐Filho, Joyce Villa Verde Bastos Borba, Scott S. Auerbach and Charles Schmitt. Their work appears in journals such as Journal of Chemical Information and Modeling, Bioinformatics, Drug Discovery Today, Regulatory Toxicology and Pharmacology and Blood.

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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