Michael Cantor

14.2k total citations · 3 hit papers
40 papers, 4.1k citations indexed

About

Michael Cantor is a scholar working on Molecular Biology, Artificial Intelligence and General Health Professions. According to data from OpenAlex, Michael Cantor has authored 40 papers receiving a total of 4.1k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 9 papers in Artificial Intelligence and 6 papers in General Health Professions. Recurrent topics in Michael Cantor's work include Biomedical Text Mining and Ontologies (15 papers), Bioinformatics and Genomic Networks (8 papers) and Semantic Web and Ontologies (8 papers). Michael Cantor is often cited by papers focused on Biomedical Text Mining and Ontologies (15 papers), Bioinformatics and Genomic Networks (8 papers) and Semantic Web and Ontologies (8 papers). Michael Cantor collaborates with scholars based in United States, Canada and United Kingdom. Michael Cantor's co-authors include David Botstein, Pat Brown, Robert Tibshirani, Russ B. Altman, Olga G. Troyanskaya, Gavin Sherlock, Trevor Hastie, Lorna E. Thorpe, Alexander Poliakov and Henrik Nordberg and has published in prestigious journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Michael Cantor

40 papers receiving 3.9k citations

Hit Papers

Missing value estimation methods for DNA microarrays 2001 2026 2009 2017 2001 2013 2018 500 1000 1.5k 2.0k 2.5k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael Cantor United States 15 1.9k 682 339 261 216 40 4.1k
Daniel J. Stekhoven Switzerland 13 605 0.3× 644 0.9× 272 0.8× 174 0.7× 148 0.7× 26 4.4k
Joon Lee United States 45 1.7k 0.9× 558 0.8× 294 0.9× 301 1.2× 341 1.6× 238 7.2k
Fang Liu China 44 1.6k 0.8× 474 0.7× 221 0.7× 472 1.8× 245 1.1× 401 6.8k
Pat Brown United States 8 1.7k 0.9× 599 0.9× 341 1.0× 73 0.3× 76 0.4× 26 3.4k
Mahlet G. Tadesse United States 31 1.3k 0.7× 430 0.6× 266 0.8× 67 0.3× 99 0.5× 95 3.6k
Riccardo Bellazzi Italy 46 2.2k 1.1× 1.9k 2.8× 626 1.8× 84 0.3× 427 2.0× 370 8.6k
Witold R. Rudnicki Poland 14 1.1k 0.5× 660 1.0× 186 0.5× 230 0.9× 38 0.2× 44 4.7k
Ping Zhang China 37 1.8k 0.9× 234 0.3× 348 1.0× 582 2.2× 81 0.4× 267 5.4k
Matthias Schmid Germany 42 1.1k 0.5× 704 1.0× 271 0.8× 65 0.2× 123 0.6× 321 6.6k
Chun‐Houh Chen Taiwan 35 1.6k 0.8× 277 0.4× 340 1.0× 115 0.4× 41 0.2× 103 4.2k

Countries citing papers authored by Michael Cantor

Since Specialization
Citations

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

Fields of papers citing papers by Michael Cantor

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Cantor

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Cantor. A scholar is included among the top collaborators of Michael Cantor 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 Michael Cantor. Michael Cantor 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
2.
Li, Dadong, Donna M. Wolk, & Michael Cantor. (2020). Comparing Clinical Characteristics of Influenza and Common Coronavirus Infections Using Electronic Health Records. The Journal of Infectious Diseases. 223(11). 1879–1886. 3 indexed citations
3.
Dicker, Adam P., et al.. (2019). Big Data From Small Devices: The Future of Smartphones in Oncology. Seminars in Radiation Oncology. 29(4). 338–347. 26 indexed citations
4.
Al‐Ajlouni, Yazan A., John A. Schneider, William C. Goedel, et al.. (2018). Partner meeting venue typology and sexual risk behaviors among French men who have sex with men. International Journal of STD & AIDS. 29(13). 1282–1288. 4 indexed citations
5.
Cantor, Michael & Lorna E. Thorpe. (2018). Integrating Data On Social Determinants Of Health Into Electronic Health Records. Health Affairs. 37(4). 585–590. 217 indexed citations breakdown →
6.
Cantor, Michael, et al.. (2018). Measuring Patient and Staff Satisfaction Before and After Implementation of a Paperless Registration System. Journal of Healthcare Management. 63(3). e20–e30. 6 indexed citations
7.
Lipschitz, Jeremy, et al.. (2016). Determining the natural history of pancreatic cystic neoplasms: a Manitoban cohort study. HPB. 18(4). 383–388. 9 indexed citations
8.
Bhattacharya, Sanmitra & Michael Cantor. (2013). Analysis of eligibility criteria representation in industry-standard clinical trial protocols. Journal of Biomedical Informatics. 46(5). 805–813. 23 indexed citations
9.
Desai, Jayesh, et al.. (2013). Creation and implementation of a historical controls database from randomized clinical trials. Journal of the American Medical Informatics Association. 20(e1). e162–e168. 12 indexed citations
10.
Cantor, Michael. (2012). Translational informatics: an industry perspective. Journal of the American Medical Informatics Association. 19(2). 153–155. 3 indexed citations
11.
Harland, Lee, Christopher Larminie, Susanna‐Assunta Sansone, et al.. (2011). Empowering industrial research with shared biomedical vocabularies. Drug Discovery Today. 16(21-22). 940–947. 11 indexed citations
12.
Roy, Kevin, et al.. (2008). Implementing Online Medication Reconciliation at a Large Academic Medical Center. The Joint Commission Journal on Quality and Patient Safety. 34(9). 499–508. 25 indexed citations
13.
Cantor, Michael, et al.. (2007). Using trigger phrases to detect adverse drug reactions in ambulatory care notes. BMJ Quality & Safety. 16(2). 132–134. 22 indexed citations
14.
Singh, Harminder, et al.. (2006). Malnutrition is prevalent in hospitalized medical patients: Are housestaff identifying the malnourished patient?. Nutrition. 22(4). 350–354. 145 indexed citations
15.
Cantor, Michael, et al.. (2005). Barriers to Implementing a Surgical Beta-Blocker Protocol. The Joint Commission Journal on Quality and Patient Safety. 31(11). 640–648. 4 indexed citations
16.
Sarkar, Indra Neil, Michael Cantor, Rony Gelman, Frank W. Hartel, & Yves A. Lussier. (2003). Linking Biomedical Language Information and Knowledge Resources in the 21st Century: GO and UMLS. 427–450. 4 indexed citations
17.
Peleg, Mor, Michael Cantor, Giora Landesberg, et al.. (2003). GESDOR - a generic execution model for sharing of computer-interpretable clinical practice guidelines.. PubMed. 694–8. 21 indexed citations
18.
Cantor, Michael & Yves A. Lussier. (2003). Putting data integration into practice: using biomedical terminologies to add structure to existing data sources.. PubMed. 125–9. 16 indexed citations
19.
Sarkar, Indra Neil, Michael Cantor, Rochel Gelman, Frank W. Hartel, & Yves A. Lussier. (2002). LINKING BIOMEDICAL LANGUAGE INFORMATION AND KNOWLEDGE RESOURCES: GO AND UMLS. PubMed. 439–450. 22 indexed citations
20.
Troyanskaya, Olga G., Michael Cantor, Gavin Sherlock, et al.. (2001). Missing value estimation methods for DNA microarrays. Bioinformatics. 17(6). 520–525. 2794 indexed citations breakdown →

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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