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.
Intrinsically disordered protein
20011.8k citationsA. Keith Dunker, Pedro Romero et al.profile →
Sequence complexity of disordered protein
20001.4k citationsPedro Romero, Zoran Obradović et al.profile →
Flexible nets
2005913 citationsA. Keith Dunker, Marc S. Cortese et al.profile →
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).
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.
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.
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 →
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
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
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
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
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.