Daniel C. Berrios

1.0k total citations
31 papers, 532 citations indexed

About

Daniel C. Berrios is a scholar working on Artificial Intelligence, Physiology and Molecular Biology. According to data from OpenAlex, Daniel C. Berrios has authored 31 papers receiving a total of 532 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 9 papers in Physiology and 8 papers in Molecular Biology. Recurrent topics in Daniel C. Berrios's work include Spaceflight effects on biology (9 papers), Semantic Web and Ontologies (9 papers) and Biomedical Text Mining and Ontologies (7 papers). Daniel C. Berrios is often cited by papers focused on Spaceflight effects on biology (9 papers), Semantic Web and Ontologies (9 papers) and Biomedical Text Mining and Ontologies (7 papers). Daniel C. Berrios collaborates with scholars based in United States, United Kingdom and Hong Kong. Daniel C. Berrios's co-authors include Sylvain V. Costes, Samrawit Gebre, Jonathan M. Galazka, Afshin Beheshti, Homer Fogle, Shayoni Ray, Kirill Grigorev, Lawrence M. Fagan, Peter Tran and Youngmi Kim Pak and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and PLoS ONE.

In The Last Decade

Daniel C. Berrios

29 papers receiving 487 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel C. Berrios United States 12 168 120 118 76 65 31 532
Weixin Li United States 13 45 0.3× 19 0.2× 91 0.8× 23 0.3× 32 0.5× 37 552
Bobbie Macdonald United States 7 53 0.3× 66 0.6× 27 0.2× 33 0.4× 82 1.3× 8 652
Carel F.W. Peeters Netherlands 16 91 0.5× 23 0.2× 151 1.3× 43 0.6× 36 0.6× 46 724
Stylianos Serghiou United States 13 43 0.3× 25 0.2× 175 1.5× 101 1.3× 41 0.6× 23 1.0k
Erick R. Scott United States 13 95 0.6× 15 0.1× 186 1.6× 16 0.2× 22 0.3× 21 710
Evelyne Bischof China 11 61 0.4× 17 0.1× 123 1.0× 11 0.1× 6 0.1× 32 437
Isabelle Budin‐Ljøsne Norway 14 223 1.3× 24 0.2× 80 0.7× 90 1.2× 65 1.0× 35 772
Chaohui Lü China 11 15 0.1× 35 0.3× 80 0.7× 17 0.2× 33 0.5× 38 403
Anne-Marie Tassé Canada 8 58 0.3× 20 0.2× 128 1.1× 69 0.9× 20 0.3× 15 466
Rachel MacKay Altman Canada 9 34 0.2× 10 0.1× 81 0.7× 115 1.5× 8 0.1× 21 452

Countries citing papers authored by Daniel C. Berrios

Since Specialization
Citations

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

Fields of papers citing papers by Daniel C. Berrios

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel C. Berrios

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel C. Berrios. A scholar is included among the top collaborators of Daniel C. Berrios 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 Daniel C. Berrios. Daniel C. Berrios 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.
Kiffer, Frederico, Daniel C. Berrios, Sylvain V. Costes, et al.. (2023). Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium. International Journal of Radiation Biology. 99(8). 1291–1300. 4 indexed citations
2.
Berrios, Daniel C., Eric Weitz, Kirill Grigorev, et al.. (2020). Visualizing Omics Data from Spaceflight Samples using the NASA GeneLab Platform. EPiC series in computing. 70. 89–78. 2 indexed citations
3.
Beheshti, Afshin, Yasaman Shirazi‐Fard, Sungshin Choi, et al.. (2019). Exploring the Effects of Spaceflight on Mouse Physiology using the Open Access NASA GeneLab Platform. Journal of Visualized Experiments. 13 indexed citations
4.
Beheshti, Afshin, Shayoni Ray, Homer Fogle, Daniel C. Berrios, & Sylvain V. Costes. (2018). A microRNA signature and TGF-β1 response were identified as the key master regulators for spaceflight response. PLoS ONE. 13(7). e0199621–e0199621. 32 indexed citations
5.
Beheshti, Afshin, J. Miller, Yared H. Kidane, et al.. (2018). NASA GeneLab Project: Bridging Space Radiation Omics with Ground Studies. Radiation Research. 189(6). 553–559. 24 indexed citations
6.
Keller, Richard M., et al.. (2008). XSearch: A System for Searching and Interrelating NASA Mission Operations Data. 3 indexed citations
7.
Berrios, Daniel C., et al.. (2007). Capturing Collaborative Decision-Making Rationale during Science Mission Operations. 1 indexed citations
8.
Keller, Richard M., et al.. (2006). Semantic Integration of Heterogeneous NASA Mission Data Sources.. National Conference on Artificial Intelligence. 108–109. 1 indexed citations
9.
Sierhuis, Maarten, William J. Clancey, Daniel C. Berrios, et al.. (2005). NASA's Mobile Agents Architecture: A Multi-Agent Workflow and Communication System for Planetary Exploration. 603. 62. 9 indexed citations
10.
Clancey, William J., Maarten Sierhuis, John Dowding, et al.. (2005). Automating CapCom Using Mobile Agents and Robotic Assistants. NASA STI Repository (National Aeronautics and Space Administration). 38 indexed citations
11.
Berrios, Daniel C. & Richard M. Keller. (2003). Developing A Web-based User Interface for Semantic Information Retrieval. NASA STI Repository (National Aeronautics and Space Administration).
12.
Berrios, Daniel C.. (2002). Methods for Semi-automated Indexing for High Precision Information Retrieval. Journal of the American Medical Informatics Association. 9(6). 637–652. 17 indexed citations
13.
Fagan, Lawrence M., et al.. (2001). Information Retrieval Using UMLS-based Structured Queries.. Europe PMC (PubMed Central). 902–902. 1 indexed citations
14.
Berrios, Daniel C., et al.. (2001). Methods For Semi-Automated Index Generation For High Precision Information Retrieval. 1 indexed citations
15.
Berrios, Daniel C.. (2000). Automated indexing for full text information retrieval.. PubMed. 71–5. 15 indexed citations
16.
Berrios, Daniel C., Andrew Kehler, & Lawrence M. Fagan. (1999). Knowledge requirements for automated inference of medical textbook markup.. PubMed. 676–80. 11 indexed citations
17.
Berrios, Daniel C., Andrew Kehler, David Kim, Victor L. Yu, & Lawrence M. Fagan. (1998). Automated Text Markup for Information Retrieval from an Electronic Textbook of Infectious Disease. PubMed Central. 975–975. 5 indexed citations
18.
Kim, David, et al.. (1998). MYCIN II: design and implementation of a therapy reference with complex content-based indexing.. PubMed. 175–9. 7 indexed citations
19.
Pak, Youngmi Kim, et al.. (1996). Activation of LDL receptor gene expression in HepG2 cells by hepatocyte growth factor. Journal of Lipid Research. 37(5). 985–998. 44 indexed citations
20.
Berrios, Daniel C., et al.. (1991). Domestic violence. Risk factors and outcomes.. PubMed. 155(2). 133–5. 138 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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