Darrell E. Hurt

4.7k total citations · 2 hit papers
51 papers, 3.3k citations indexed

About

Darrell E. Hurt is a scholar working on Molecular Biology, Infectious Diseases and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Darrell E. Hurt has authored 51 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 13 papers in Infectious Diseases and 11 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Darrell E. Hurt's work include Tuberculosis Research and Epidemiology (10 papers), Antimicrobial Peptides and Activities (8 papers) and COVID-19 diagnosis using AI (8 papers). Darrell E. Hurt is often cited by papers focused on Tuberculosis Research and Epidemiology (10 papers), Antimicrobial Peptides and Activities (8 papers) and COVID-19 diagnosis using AI (8 papers). Darrell E. Hurt collaborates with scholars based in United States, Greece and Georgia. Darrell E. Hurt's co-authors include Tomoshige Kino, George P. Chrousos, Nader D. Nader, Takamasa Ichijo, Alex Rosenthal, Andrei Gabrielian, Takehiko Ueyama, Thomas L. Leto, Stanislas Morand and Michael Tartakovsky and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Journal of Biological Chemistry.

In The Last Decade

Darrell E. Hurt

49 papers receiving 3.3k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Noncoding RNA Gas5 Is a Growth Arrest– and Starvation-Associated Repressor of the Glucocorticoid Receptor

2010 1.0k citations Tomoshige Kino, Darrell E. Hurt et al. profile →
DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics

2020 390 citations Malak Pirtskhalava, Boris Vishnepolsky et al. Nucleic Acids Research profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Darrell E. Hurt United States	25	2.1k	907	573	386	289	51	3.3k
Mohammad Hossein Modarressi Iran	36	2.3k 1.1×	578 0.6×	155 0.3×	533 1.4×	360 1.2×	202	3.7k
Jacob Odeberg Sweden	31	1.3k 0.6×	350 0.4×	797 1.4×	828 2.1×	336 1.2×	99	3.3k
Lina Wang China	35	2.4k 1.2×	583 0.6×	242 0.4×	978 2.5×	465 1.6×	165	4.2k
Hao Zeng China	28	1.1k 0.5×	328 0.4×	342 0.6×	491 1.3×	289 1.0×	173	2.8k
Jian Huang China	42	3.9k 1.9×	358 0.4×	161 0.3×	425 1.1×	302 1.0×	333	6.1k
Fabrício F. Costa United States	34	2.5k 1.2×	1.1k 1.2×	229 0.4×	326 0.8×	154 0.5×	70	3.4k
Bin Jiang China	25	1.1k 0.5×	315 0.3×	406 0.7×	327 0.8×	332 1.1×	80	2.7k
Yan Su United States	27	2.4k 1.2×	384 0.4×	67 0.1×	457 1.2×	331 1.1×	75	4.2k
Fang Wang China	29	1.8k 0.9×	438 0.5×	64 0.1×	389 1.0×	300 1.0×	153	3.6k
Amit Gaggar United States	36	1.3k 0.6×	539 0.6×	115 0.2×	875 2.3×	399 1.4×	96	4.3k

Countries citing papers authored by Darrell E. Hurt

Since Specialization

Citations

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

Fields of papers citing papers by Darrell E. Hurt

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Darrell E. Hurt. 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 Darrell E. Hurt. The network helps show where Darrell E. Hurt may publish in the future.

Co-authorship network of co-authors of Darrell E. Hurt

This figure shows the co-authorship network connecting the top 25 collaborators of Darrell E. Hurt. A scholar is included among the top collaborators of Darrell E. Hurt 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 Darrell E. Hurt. Darrell E. Hurt is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Vishnepolsky, Boris, Andrei Gabrielian, Alex Rosenthal, et al.. (2024). Evaluation of the synergistic potential and mechanisms of action for de novo designed cationic antimicrobial peptides. Heliyon. 10(6). e27852–e27852. 4 indexed citations

Lowekamp, Bradley, Andrei Gabrielian, Darrell E. Hurt, Alex Rosenthal, & Ziv Yaniv. (2024). Tuberculosis chest x-ray image retrieval system using deep learning based biomarker predictions. PubMed. 12931. 32–32.

Rosenfeld, Gabriel, Andrei Gabrielian, Darrell E. Hurt, & Alex Rosenthal. (2023). Predictive capabilities of baseline radiological findings for early and late disease outcomes within sensitive and multi-drug resistant tuberculosis cases. European Journal of Radiology Open. 11. 100518–100518. 1 indexed citations

Vishnepolsky, Boris, Andrei Gabrielian, Alex Rosenthal, et al.. (2023). Analysis, Modeling, and Target-Specific Predictions of Linear Peptides Inhibiting Virus Entry. ACS Omega. 8(48). 46218–46226. 3 indexed citations

Vishnepolsky, Boris, Andrei Gabrielian, Alex Rosenthal, et al.. (2022). Comparative analysis of machine learning algorithms on the microbial strain-specific AMP prediction. Briefings in Bioinformatics. 23(4). 26 indexed citations

Cruz, Phillip, et al.. (2022). Feasibility of virtual reality based training for optimising COVID-19 case handling in Uganda. BMC Medical Education. 22(1). 274–274. 25 indexed citations

Rosenfeld, Gabriel, et al.. (2021). Radiologist observations of computed tomography (CT) images predict treatment outcome in TB Portals, a real-world database of tuberculosis (TB) cases. PLoS ONE. 16(3). e0247906–e0247906. 11 indexed citations

Yang, Feng, Hang Yu, Manohar Karki, et al.. (2021). Differentiating between drug-sensitive and drug-resistant tuberculosis with machine learning for clinical and radiological features. Quantitative Imaging in Medicine and Surgery. 12(1). 675–687. 17 indexed citations

Engle, Eric, et al.. (2020). The technology behind TB DEPOT: a novel public analytics platform integrating tuberculosis clinical, genomic, and radiological data for visual and statistical exploration. Journal of the American Medical Informatics Association. 28(1). 71–79. 5 indexed citations

10.

Pirtskhalava, Malak, Boris Vishnepolsky, Andrei Gabrielian, et al.. (2020). DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics. Nucleic Acids Research. 49(D1). D288–D297. 390 indexed citations breakdown →

11.

Armani, Andrea M., et al.. (2020). Low-tech solutions for the COVID-19 supply chain crisis. Nature Reviews Materials. 5(6). 403–406. 91 indexed citations

12.

Gabrielian, Andrei, Eric Engle, Michael A. Harris, et al.. (2019). TB DEPOT (Data Exploration Portal): A multi-domain tuberculosis data analysis resource. PLoS ONE. 14(5). e0217410–e0217410. 13 indexed citations

13.

Gabrielian, Andrei, Eric Engle, Michael A. Harris, et al.. (2019). Comparative analysis of genomic variability for drug-resistant strains of Mycobacterium tuberculosis: The special case of Belarus. Infection Genetics and Evolution. 78. 104137–104137. 5 indexed citations

14.

Weber, Nick, Jennifer Dommer, Philip MacMenamin, et al.. (2017). Nephele: a cloud platform for simplified, standardized and reproducible microbiome data analysis. Bioinformatics. 34(8). 1411–1413. 107 indexed citations

15.

Hurt, Darrell E., Shigeru Suzuki, Takafumi Mayama, Evangelia Charmandari, & Tomoshige Kino. (2016). Structural Analysis on the Pathologic Mutant Glucocorticoid Receptor Ligand-Binding Domains. Molecular Endocrinology. 30(2). 173–188. 14 indexed citations

16.

Singh, Satya P., John F. Foley, Hongwei H. Zhang, et al.. (2015). Selectivity in the Use of Gi/o Proteins Is Determined by the DRF Motif in CXCR6 and Is Cell-Type Specific. Molecular Pharmacology. 88(5). 894–910. 13 indexed citations

17.

Coakley, Meghan F., Darrell E. Hurt, Nick Weber, et al.. (2014). The NIH 3D Print Exchange: A Public Resource for Bioscientific and Biomedical 3D Prints. 3D Printing and Additive Manufacturing. 1(3). 137–140. 62 indexed citations

18.

Cimbro, Raffaello, Michael Dolan, Christina Guzzo, et al.. (2014). Tyrosine sulfation in the second variable loop (V2) of HIV-1 gp120 stabilizes V2–V3 interaction and modulates neutralization sensitivity. Proceedings of the National Academy of Sciences. 111(8). 3152–3157. 24 indexed citations

19.

Yoo, Terry S., Trevor D. Hamilton, Darrell E. Hurt, et al.. (2011). Toward quantitative X-ray CT phantoms of metastatic tumors using rapid prototyping technology. 1770–1773. 16 indexed citations

20.

Das, Suman R., Pere Puigbò, Scott E. Hensley, et al.. (2010). Glycosylation Focuses Sequence Variation in the Influenza A Virus H1 Hemagglutinin Globular Domain. PLoS Pathogens. 6(11). e1001211–e1001211. 93 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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