Peggy Peissig

8.3k total citations
91 papers, 2.8k citations indexed

About

Peggy Peissig is a scholar working on Molecular Biology, Genetics and Artificial Intelligence. According to data from OpenAlex, Peggy Peissig has authored 91 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Molecular Biology, 32 papers in Genetics and 27 papers in Artificial Intelligence. Recurrent topics in Peggy Peissig's work include Biomedical Text Mining and Ontologies (22 papers), Genetic Associations and Epidemiology (20 papers) and Machine Learning in Healthcare (15 papers). Peggy Peissig is often cited by papers focused on Biomedical Text Mining and Ontologies (22 papers), Genetic Associations and Epidemiology (20 papers) and Machine Learning in Healthcare (15 papers). Peggy Peissig collaborates with scholars based in United States, Portugal and Poland. Peggy Peissig's co-authors include Joshua C. Denny, Luke V. Rasmussen, Catherine A. McCarty, Jennifer A. Pacheco, Richard L. Berg, Abel Kho, Christopher G. Chute, Jyotishman Pathak, Iftikhar J. Kullo and Suzette J. Bielinski and has published in prestigious journals such as Nature Communications, PLoS ONE and Journal of Allergy and Clinical Immunology.

In The Last Decade

Peggy Peissig

91 papers receiving 2.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Peggy Peissig United States 28 972 844 620 531 314 91 2.8k
Jennifer A. Pacheco United States 22 814 0.8× 772 0.9× 417 0.7× 521 1.0× 280 0.9× 63 2.2k
Abel Kho United States 29 641 0.7× 608 0.7× 441 0.7× 534 1.0× 480 1.5× 126 3.1k
Melissa Basford United States 19 942 1.0× 537 0.6× 803 1.3× 373 0.7× 218 0.7× 36 2.6k
Wei‐Qi Wei United States 25 1.1k 1.1× 680 0.8× 1.0k 1.6× 379 0.7× 520 1.7× 100 4.1k
Luke V. Rasmussen United States 24 671 0.7× 685 0.8× 384 0.6× 497 0.9× 236 0.8× 83 2.1k
Vivian S. Gainer United States 40 1.1k 1.2× 1.0k 1.2× 1.0k 1.7× 604 1.1× 967 3.1× 95 4.7k
Peter Bjødstrup Jensen Denmark 19 817 0.8× 865 1.0× 179 0.3× 481 0.9× 427 1.4× 49 2.7k
Daniel R. Masys United States 28 816 0.8× 284 0.3× 714 1.2× 348 0.7× 263 0.8× 75 3.0k
Vasa Ćurčin United Kingdom 29 441 0.5× 496 0.6× 240 0.4× 385 0.7× 591 1.9× 140 3.0k
Suzette J. Bielinski United States 33 844 0.9× 413 0.5× 614 1.0× 269 0.5× 440 1.4× 154 3.7k

Countries citing papers authored by Peggy Peissig

Since Specialization
Citations

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

Fields of papers citing papers by Peggy Peissig

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peggy Peissig

This figure shows the co-authorship network connecting the top 25 collaborators of Peggy Peissig. A scholar is included among the top collaborators of Peggy Peissig 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 Peggy Peissig. Peggy Peissig 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.
Page, David, et al.. (2021). Identifying Adverse Drug Events by Relational Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 26(1). 1599–1605. 1 indexed citations
2.
Zhang, Wei, et al.. (2020). Adverse drug reaction discovery from electronic health records with deep neural networks. PubMed. 2020. 30–39. 5 indexed citations
3.
Taylor, Casey Overby, K Lemke, Thomas M. Richards, et al.. (2019). Comorbidity Characterization Among eMERGE Institutions: A Pilot Evaluation with the Johns Hopkins Adjusted Clinical Groups® System.. PubMed. 2019. 145–152. 4 indexed citations
4.
Bao, Yujia, James Thomson, Michael D. Caldwell, et al.. (2018). A Machine-Learning-Based Drug Repurposing Approach Using Baseline Regularization. Methods in molecular biology. 1903. 255–267. 14 indexed citations
5.
Peissig, Peggy, et al.. (2018). Temporal Poisson Square Root Graphical Models.. PubMed. 80. 1714–1723. 4 indexed citations
6.
Almoguera, Berta, Lyam Vazquez, Frank Mentch, et al.. (2018). Novel locus for atopic dermatitis in African Americans and replication in European Americans. Journal of Allergy and Clinical Immunology. 143(3). 1229–1231. 8 indexed citations
7.
Peissig, Peggy, Catherine A. McCarty, Jennifer A. Pacheco, et al.. (2017). Prototype Development: Context-Driven Dynamic XML Ophthalmologic Data Capture Application. JMIR Medical Informatics. 5(3). e27–e27. 2 indexed citations
8.
Bao, Yujia, et al.. (2017). Hawkes Process Modeling of Adverse Drug Reactions with Longitudinal Observational Data. 177–190. 5 indexed citations
9.
Alatawi, Yasser, Ning Cheng, Jingjing Qian, et al.. (2017). Methodological Considerations for Comparison of Brand Versus Generic Versus Authorized Generic Adverse Event Reports in the US Food and Drug Administration Adverse Event Reporting System (FAERS). Clinical Drug Investigation. 37(12). 1143–1152. 11 indexed citations
10.
Overby, Casey Lynnette, John J. Connolly, Christopher G. Chute, et al.. (2016). Practical considerations for implementing genomic information resources. Applied Clinical Informatics. 7(3). 870–882. 22 indexed citations
11.
Wu, Yirong, Craig K. Abbey, Jie Liu, et al.. (2016). Discriminatory power of common genetic variants in personalized breast cancer diagnosis. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9787. 978706–978706. 6 indexed citations
12.
Thomson, James, et al.. (2016). Baseline Regularization for Computational Drug Repositioning with Longitudinal Observational Data.. PubMed. 2016. 2521–2528. 9 indexed citations
13.
Burnside, Elizabeth S., Jie Liu, Yirong Wu, et al.. (2015). Comparing Mammography Abnormality Features to Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy. Academic Radiology. 23(1). 62–69. 10 indexed citations
14.
Liu, Jie, Yirong Wu, Irene M. Ong, et al.. (2015). Leveraging Interaction between Genetic Variants and Mammographic Findings for Personalized Breast Cancer Diagnosis.. PubMed. 2015. 107–11. 3 indexed citations
15.
Wu, Yirong, Craig K. Abbey, Xianqiao Chen, et al.. (2015). Developing a utility decision framework to evaluate predictive models in breast cancer risk estimation. Journal of Medical Imaging. 2(4). 41005–41005. 5 indexed citations
16.
Shameer, Khader, Joshua C. Denny, Keyue Ding, et al.. (2013). A genome- and phenome-wide association study to identify genetic variants influencing platelet count and volume and their pleiotropic effects. Human Genetics. 133(1). 95–109. 98 indexed citations
17.
Kho, Abel, Luke V. Rasmussen, John J. Connolly, et al.. (2013). Practical challenges in integrating genomic data into the electronic health record. Genetics in Medicine. 15(10). 772–778. 69 indexed citations
18.
Davis, Jesse, Elizabeth Berg, David Page, et al.. (2011). Discovering latent structure in clinical databases. Lirias (KU Leuven). 8. 2 indexed citations
19.
Waudby, Carol, Richard L. Berg, James G. Linneman, et al.. (2011). Cataract research using electronic health records. BMC Ophthalmology. 11(1). 32–32. 30 indexed citations
20.
Wilke, Russell A., Richard L. Berg, Peggy Peissig, et al.. (2007). Use of an Electronic Medical Record for the Identification of Research Subjects with Diabetes Mellitus. Clinical Medicine & Research. 5(1). 1–7. 62 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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