Pedro Romero

15.7k total citations · 8 hit papers
65 papers, 11.3k citations indexed

About

Pedro Romero is a scholar working on Molecular Biology, Immunology and Materials Chemistry. According to data from OpenAlex, Pedro Romero has authored 65 papers receiving a total of 11.3k indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Molecular Biology, 16 papers in Immunology and 16 papers in Materials Chemistry. Recurrent topics in Pedro Romero's work include Protein Structure and Dynamics (35 papers), RNA and protein synthesis mechanisms (20 papers) and Enzyme Structure and Function (16 papers). Pedro Romero is often cited by papers focused on Protein Structure and Dynamics (35 papers), RNA and protein synthesis mechanisms (20 papers) and Enzyme Structure and Function (16 papers). Pedro Romero collaborates with scholars based in United States, Switzerland and Russia. Pedro Romero's co-authors include Zoran Obradović, A. Keith Dunker, Vladimir N. Uversky, Ethan C. Garner, Celeste J. Brown, Christopher J. Oldfield, Marc S. Cortese, A. Keith Dunker, A. Keith Dunker and Xiaohong Li and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and SHILAP Revista de lepidopterología.

In The Last Decade

Pedro Romero

63 papers receiving 11.2k citations

Hit Papers

Intrinsically disordered protein 2000 2026 2008 2017 2001 2000 2005 2000 2008 500 1000 1.5k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pedro Romero United States 41 9.1k 2.6k 1.1k 970 845 65 11.3k
Simon C. Lovell United Kingdom 29 9.0k 1.0× 2.6k 1.0× 843 0.8× 857 0.9× 1.1k 1.3× 77 12.0k
Tom Alber United States 62 10.3k 1.1× 3.0k 1.2× 1.2k 1.1× 884 0.9× 1.2k 1.4× 131 13.1k
James U. Bowie United States 62 13.8k 1.5× 2.4k 0.9× 1.3k 1.2× 803 0.8× 1.6k 1.9× 157 17.4k
István Simon Hungary 46 10.2k 1.1× 2.1k 0.8× 1.2k 1.1× 547 0.6× 1.2k 1.4× 144 13.1k
Roland L. Dunbrack United States 52 13.6k 1.5× 4.1k 1.6× 1.1k 1.0× 976 1.0× 808 1.0× 159 16.7k
Frank DiMaio United States 60 9.6k 1.1× 2.4k 0.9× 983 0.9× 864 0.9× 1.1k 1.3× 150 13.6k
Arne Elofsson Sweden 61 11.9k 1.3× 2.4k 0.9× 946 0.9× 653 0.7× 1.3k 1.6× 181 14.6k
András Fiser United States 43 8.0k 0.9× 1.9k 0.7× 829 0.8× 1.2k 1.2× 687 0.8× 144 11.3k
Dina Schneidman‐Duhovny United States 41 7.2k 0.8× 1.8k 0.7× 582 0.5× 701 0.7× 605 0.7× 84 10.0k
Brian Kuhlman United States 60 11.7k 1.3× 3.4k 1.3× 1.0k 0.9× 589 0.6× 732 0.9× 164 14.5k

Countries citing papers authored by Pedro Romero

Since Specialization
Citations

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

Fields of papers citing papers by Pedro Romero

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pedro Romero

This figure shows the co-authorship network connecting the top 25 collaborators of Pedro Romero. A scholar is included among the top collaborators of Pedro Romero 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 Pedro Romero. Pedro Romero 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.
Ulrich, Eldon L., Kumaran Baskaran, Hesam Dashti, et al.. (2018). NMR-STAR: comprehensive ontology for representing, archiving and exchanging data from nuclear magnetic resonance spectroscopic experiments. Journal of Biomolecular NMR. 73(1-2). 5–9. 25 indexed citations
2.
Maciejewski, Mark W., Adam D. Schuyler, Michael R. Gryk, et al.. (2017). NMRbox: A Resource for Biomolecular NMR Computation. Biophysical Journal. 112(8). 1529–1534. 322 indexed citations breakdown →
3.
Huang, Fei, Christopher J. Oldfield, Bin Xue, et al.. (2014). Improving protein order-disorder classification using charge-hydropathy plots. PMC.
5.
Oates, Matt E., Pedro Romero, Takashi Ishida, et al.. (2012). D2P2: database of disordered protein predictions. Nucleic Acids Research. 41(D1). D508–D516. 524 indexed citations breakdown →
6.
Huang, Fei, Christopher J. Oldfield, Jingwei Meng, et al.. (2011). SUBCLASSIFYING DISORDERED PROTEINS BY THE CH-CDF PLOT METHOD. PubMed. 128–139. 78 indexed citations
7.
Dunker, A. Keith, Christopher J. Oldfield, Jingwei Meng, et al.. (2008). The unfoldomics decade: an update on intrinsically disordered proteins. TUScholarShare (Temple University). 13 indexed citations
8.
Bron, Luc & Pedro Romero. (2006). [Immunotherapy for head and neck squamous cell carcinoma].. PubMed. 2(81). 2216–9. 1 indexed citations
9.
Romero, Pedro, Vladimir N. Uversky, Predrag Radivojac, et al.. (2006). Alternative splicing in concert with protein intrinsic disorder enables increased functional diversity in multicellular organisms. Proceedings of the National Academy of Sciences. 103(22). 8390–8395. 355 indexed citations
10.
Barker, Ken, Vinay K. Chaudhri, Peter E. Clark, et al.. (2004). A question-answering system for AP chemistry: assessing KR&R technologies. Principles of Knowledge Representation and Reasoning. 488–497. 24 indexed citations
11.
12.
Romero, Pedro, Zoran Obradović, & A. Keith Dunker. (2004). Natively Disordered Proteins. PubMed. 3(2). 105–113. 129 indexed citations
13.
Ayyoub, Maha, Marco Migliaccio, Philippe Guillaume, et al.. (2001). Lack of tumor recognition by hTERT peptide 540-548-specific CD8+ T cells from melanoma patients reveals inefficient antigen processing. European Journal of Immunology. 31(9). 2642–2651. 71 indexed citations
14.
Valmori, Danila, Mikäel J. Pittet, Cédric Vonarbourg, et al.. (1999). Analysis of the cytolytic T lymphocyte response of melanoma patients to the naturally HLA-A*0201-associated tyrosinase peptide 368-376.. PubMed. 59(16). 4050–5. 63 indexed citations
15.
D’Souza, Sushila, Donata Rimoldi, Danielle Líénard, et al.. (1998). Circulating MELAN-A/MART-1 specific cytolytic T lymphocyte precursors in HLA-A2+ melanoma patients have a memory phenotype. International Journal of Cancer. 78(6). 699–706. 52 indexed citations
16.
Rani, Meeta, Pedro Romero, Zoran Obradović, & A. Keith Dunker. (1998). Annotation of PDB with respect to "Disordered Regions" in Proteins. Proceedings Genome Informatics Workshop/Genome informatics. 9. 240–241. 2 indexed citations
17.
Romero, Pedro, Zoran Obradović, Charles R. Kissinger, et al.. (1998). Thousands of proteins likely to have long disordered regions.. PubMed. 437–48. 201 indexed citations
18.
Dunker, A. Keith, Ethan C. Garner, Pedro Romero, et al.. (1998). Protein disorder and the evolution of molecular recognition: theory, predictions and observations.. PubMed. 473–84. 306 indexed citations
19.
Zarour, Hassane M., Charles De Smet, Frédéric Lehmann, et al.. (1996). The Majority of Autologous Cytolytic T-Lymphocyte Clones Derived from Peripheral Blood Lymphocytes of a Melanoma Patient Recognize an Antigenic Peptide Derived from Gene Pmel17/gp100. Journal of Investigative Dermatology. 107(1). 63–67. 47 indexed citations
20.
Auber, Miklos, Jean I. DeHaven, Peter C. Raich, et al.. (1991). IL-2/LAK cell treatment for advanced cancers with emphasis on a novel administration.. PubMed. 87(8). 344–6. 1 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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