Daniel J. Parente

587 total citations
30 papers, 374 citations indexed

About

Daniel J. Parente is a scholar working on Molecular Biology, General Health Professions and Genetics. According to data from OpenAlex, Daniel J. Parente has authored 30 papers receiving a total of 374 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 7 papers in General Health Professions and 6 papers in Genetics. Recurrent topics in Daniel J. Parente's work include Protein Structure and Dynamics (7 papers), RNA and protein synthesis mechanisms (6 papers) and Public Health Policies and Education (4 papers). Daniel J. Parente is often cited by papers focused on Protein Structure and Dynamics (7 papers), RNA and protein synthesis mechanisms (6 papers) and Public Health Policies and Education (4 papers). Daniel J. Parente collaborates with scholars based in United States, Germany and United Kingdom. Daniel J. Parente's co-authors include Liskin Swint‐Kruse, Michael W. Manley, Marwan Shinawi, Berivan Baskin, Gabriel C. Araujo, Megan T. Cho, Ganka Douglas, Sudheer Tungtur, J. Christian J. Ray and Aron W. Fenton and has published in prestigious journals such as PLoS ONE, Journal of Molecular Biology and Biophysical Journal.

In The Last Decade

Daniel J. Parente

28 papers receiving 355 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 J. Parente United States 12 227 89 40 35 29 30 374
Suqin Guo United States 15 98 0.4× 57 0.6× 8 0.2× 78 2.2× 9 0.3× 44 773
Yuehan Li China 11 213 0.9× 25 0.3× 3 0.1× 15 0.4× 26 0.9× 31 650
Kurt O. Gilliland United States 17 363 1.6× 47 0.5× 30 0.8× 9 0.3× 17 0.6× 50 786
Viktor Dombrádi Hungary 13 232 1.0× 36 0.4× 40 1.0× 2 0.1× 21 0.7× 44 454
T.J. Gorrie-Stone United Kingdom 10 496 2.2× 152 1.7× 30 0.8× 9 0.3× 30 1.0× 13 703
R. Lane Coffee United States 12 666 2.9× 111 1.2× 8 0.2× 30 0.9× 6 0.2× 17 804
Michael McCarthy United States 12 142 0.6× 15 0.2× 13 0.3× 16 0.5× 10 0.3× 49 449
Xiang Xu China 10 133 0.6× 50 0.6× 19 0.5× 11 0.3× 22 0.8× 21 503
Alexandra M. Fulton United Kingdom 12 239 1.1× 46 0.5× 11 0.3× 11 0.3× 14 0.5× 17 410
Ryan Chu Hong Kong 9 112 0.5× 73 0.8× 12 0.3× 4 0.1× 4 0.1× 22 373

Countries citing papers authored by Daniel J. Parente

Since Specialization
Citations

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

Fields of papers citing papers by Daniel J. Parente

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel J. Parente

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel J. Parente. A scholar is included among the top collaborators of Daniel J. Parente 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 J. Parente. Daniel J. Parente 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.
Parente, Daniel J., et al.. (2024). Quality, Accuracy, and Bias in ChatGPT-Based Summarization of Medical Abstracts. The Annals of Family Medicine. 22(2). 113–120. 18 indexed citations
2.
Parente, Daniel J.. (2024). Generative Artificial Intelligence and Large Language Models in Primary Care Medical Education. Family Medicine. 56(9). 534–540. 12 indexed citations
3.
Pacheco, Christina M., Edward F. Ellerbeck, Elizabeth Ablah, et al.. (2024). Communities organizing to promote equity: engaging local communities in public health responses to health inequities exacerbated by COVID-19–protocol paper. Frontiers in Public Health. 12. 1369777–1369777. 2 indexed citations
4.
Parente, Daniel J.. (2024). Food Insecurity Is Associated with Vitamin B12 Deficiency: The All of Us Database. The Journal of the American Board of Family Medicine. 37(Supplement2). S156–S163.
5.
Parente, Daniel J.. (2024). Leveraging the All of Us Database for Primary Care Research with Large Datasets. The Journal of the American Board of Family Medicine. 37(Supplement2). S144–S155. 1 indexed citations
6.
Chartash, David, et al.. (2024). Family Medicine Must Prepare for Artificial Intelligence. The Journal of the American Board of Family Medicine. 37(4). 520–524. 3 indexed citations
7.
LeMaster, Joseph W., Daniel J. Parente, Christina M. Pacheco, et al.. (2023). Assessing Social Needs and Engaging Community Health Workers in Underserved Kansas Counties: Insights From Primary Care Providers and Clinic Managers. Journal of Primary Care & Community Health. 14. 4277834673–4277834673. 2 indexed citations
8.
Mabachi, Natabhona, et al.. (2023). More Than Half of Family Medicine Clerkships Do Not Address Systemic Racism: A CERA Study. Family Medicine. 55(4). 217–224. 3 indexed citations
9.
Parente, Daniel J., et al.. (2023). Adaptation and External Validation of Pathogenic Urine Culture Prediction in Primary Care Using Machine Learning. The Annals of Family Medicine. 21(1). 11–18. 6 indexed citations
10.
Parente, Daniel J., et al.. (2023). Machine Learning Prediction of Urine Cultures in Primary Care. Big Data. 4141–4141. 2 indexed citations
11.
Parente, Daniel J., et al.. (2022). Impact of the COVID-19 Pandemic on Exercise Habits Among US Primary Care Patients. The Journal of the American Board of Family Medicine. 35(2). 295–309. 2 indexed citations
12.
Parente, Daniel J., et al.. (2021). Association Between Unmet Essential Social Needs and Influenza Vaccination in US Adults. Journal of General Internal Medicine. 37(1). 23–31. 4 indexed citations
13.
Parente, Daniel J., et al.. (2020). Identification of biochemically neutral positions in liver pyruvate kinase. Proteins Structure Function and Bioinformatics. 88(10). 1340–1350. 17 indexed citations
14.
Sousa, Filipa L., et al.. (2016). Data on publications, structural analyses, and queries used to build and utilize the AlloRep database. Data in Brief. 8. 948–957. 3 indexed citations
15.
Parente, Daniel J., Berivan Baskin, Ganka Douglas, et al.. (2016). Neuroligin 2 nonsense variant associated with anxiety, autism, intellectual disability, hyperphagia, and obesity. American Journal of Medical Genetics Part A. 173(1). 213–216. 79 indexed citations
16.
Sousa, Filipa L., Daniel J. Parente, David L. Shis, et al.. (2015). AlloRep: A Repository of Sequence, Structural and Mutagenesis Data for the LacI/GalR Transcription Regulators. Journal of Molecular Biology. 428(4). 671–678. 19 indexed citations
17.
Manley, Michael W., et al.. (2014). Rheostats and Toggle Switches for Modifying Protein Function. Biophysical Journal. 106(2). 207a–207a. 1 indexed citations
18.
Parente, Daniel J. & Liskin Swint‐Kruse. (2013). Multiple Co-Evolutionary Networks Are Supported by the Common Tertiary Scaffold of the LacI/GalR Proteins. PLoS ONE. 8(12). e84398–e84398. 20 indexed citations
19.
Manley, Michael W., et al.. (2013). Rheostats and Toggle Switches for Modulating Protein Function. PLoS ONE. 8(12). e83502–e83502. 55 indexed citations
20.
Tungtur, Sudheer, Daniel J. Parente, & Liskin Swint‐Kruse. (2011). Functionally important positions can comprise the majority of a protein's architecture. Proteins Structure Function and Bioinformatics. 79(5). 1589–1608. 28 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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