Angel E. Dago

744 total citations
14 papers, 598 citations indexed

About

Angel E. Dago is a scholar working on Cancer Research, Molecular Biology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Angel E. Dago has authored 14 papers receiving a total of 598 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Cancer Research, 7 papers in Molecular Biology and 6 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Angel E. Dago's work include Cancer Genomics and Diagnostics (9 papers), Prostate Cancer Treatment and Research (5 papers) and Bacterial Genetics and Biotechnology (4 papers). Angel E. Dago is often cited by papers focused on Cancer Genomics and Diagnostics (9 papers), Prostate Cancer Treatment and Research (5 papers) and Bacterial Genetics and Biotechnology (4 papers). Angel E. Dago collaborates with scholars based in United States, United Kingdom and Mexico. Angel E. Dago's co-authors include James A. Hoch, Sivaramesh Wigneshweraraj, Enrique Morett, Martin Buck, Andrea Procaccini, Alexander Schug, Martin Weigt, Hendrik Szurmant, Elisabeth Steiner and Danielle I. Young and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Journal of Clinical Oncology.

In The Last Decade

Angel E. Dago

13 papers receiving 592 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Angel E. Dago United States 9 422 195 157 148 74 14 598
Liza Cubeddu Australia 17 854 2.0× 187 1.0× 73 0.5× 137 0.9× 37 0.5× 44 970
Mingxia Feng China 15 632 1.5× 150 0.8× 47 0.3× 123 0.8× 75 1.0× 34 827
Shinichi Kiyonari Japan 18 548 1.3× 166 0.9× 59 0.4× 109 0.7× 36 0.5× 27 679
Victoria Marsh United Kingdom 11 635 1.5× 231 1.2× 87 0.6× 259 1.8× 70 0.9× 14 850
Alexander Munishkin United States 8 617 1.5× 141 0.7× 55 0.4× 105 0.7× 97 1.3× 10 800
Н. Л. Миронова Russia 18 675 1.6× 69 0.4× 211 1.3× 156 1.1× 31 0.4× 55 889
Brandon J. Lamarche United States 10 517 1.2× 89 0.5× 66 0.4× 176 1.2× 18 0.2× 15 679
Sandra Schreiber Germany 14 504 1.2× 175 0.9× 65 0.4× 78 0.5× 102 1.4× 18 744
Elisabetta Bolli Italy 14 271 0.6× 52 0.3× 105 0.7× 200 1.4× 39 0.5× 28 574
Veronika Altmannová Czechia 15 1.0k 2.4× 142 0.7× 138 0.9× 249 1.7× 17 0.2× 23 1.1k

Countries citing papers authored by Angel E. Dago

Since Specialization
Citations

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

Fields of papers citing papers by Angel E. Dago

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Angel E. Dago

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

All Works

14 of 14 papers shown
1.
Welter, Lisa, Liya Xu, Angel E. Dago, et al.. (2020). Treatment response and tumor evolution: lessons from an extended series of multianalyte liquid biopsies in a metastatic breast cancer patient. Molecular Case Studies. 6(6). a005819–a005819. 26 indexed citations
2.
Barnett, Ethan, Joseph D. Schonhoft, Nikolaus Schultz, et al.. (2020). Prevalence and tissue concordance of BRCA2 copy number loss evaluated by single-cell, shallow whole genome sequencing of circulating tumor cells (CTCs) in castration-resistant prostate cancer (CRPC).. Journal of Clinical Oncology. 38(15_suppl). 5531–5531. 1 indexed citations
3.
Schonhoft, Joseph D., Jimmy L. Zhao, Adam Jendrisak, et al.. (2020). Morphology-Predicted Large-Scale Transition Number in Circulating Tumor Cells Identifies a Chromosomal Instability Biomarker Associated with Poor Outcome in Castration-Resistant Prostate Cancer. Cancer Research. 80(22). 4892–4903. 32 indexed citations
4.
Dittamore, Ryan, Yipeng Wang, Stephanie Daignault‐Newton, et al.. (2018). Phenotypic and genomic characterization of CTCs as a biomarker for prediction of Veliparib therapy benefit in mCRPC.. Journal of Clinical Oncology. 36(15_suppl). 5012–5012. 2 indexed citations
5.
Greene, Stephanie, Angel E. Dago, Yipeng Wang, et al.. (2016). Chromosomal Instability Estimation Based on Next Generation Sequencing and Single Cell Genome Wide Copy Number Variation Analysis. PLoS ONE. 11(11). e0165089–e0165089. 37 indexed citations
6.
Vansant, Gordon, Angel E. Dago, Jerry Lee, et al.. (2016). A single cell genomic signature to detect homologous recombination deficiency (HRD) and PARP inhibitors sensitivity using patient's circulating tumor cells (CTCs).. Journal of Clinical Oncology. 34(15_suppl). e23015–e23015. 3 indexed citations
7.
Dago, Angel E., Asya Stepansky, Anders Carlsson, et al.. (2014). Rapid Phenotypic and Genomic Change in Response to Therapeutic Pressure in Prostate Cancer Inferred by High Content Analysis of Single Circulating Tumor Cells. PLoS ONE. 9(8). e101777–e101777. 116 indexed citations
8.
Gross, Mitchell E., David B. Agus, Tanya B. Dorff, et al.. (2013). Sequential monitoring of androgen receptor expression and copy number variation in castration-resistant prostate cancer (CRPC).. Journal of Clinical Oncology. 31(15_suppl). 11047–11047. 1 indexed citations
9.
Kühn, Peter, Angel E. Dago, Asya Stepansky, et al.. (2013). Abstract 4599: Sequential monitoring of single-cell copy number variation in metastatic prostate cancer.. Cancer Research. 73(8_Supplement). 4599–4599.
10.
Dago, Angel E., Alexander Schug, Andrea Procaccini, et al.. (2012). Structural basis of histidine kinase autophosphorylation deduced by integrating genomics, molecular dynamics, and mutagenesis. Proceedings of the National Academy of Sciences. 109(26). E1733–42. 110 indexed citations
11.
Steiner, Elisabeth, Angel E. Dago, Danielle I. Young, et al.. (2011). Multiple orphan histidine kinases interact directly with Spo0A to control the initiation of endospore formation in Clostridium acetobutylicum. Molecular Microbiology. 80(3). 641–654. 107 indexed citations
12.
Dago, Angel E., Sivaramesh Wigneshweraraj, Martin Buck, & Enrique Morett. (2006). A Role for the Conserved GAFTGA Motif of AAA+ Transcription Activators in Sensing Promoter DNA Conformation. Journal of Biological Chemistry. 282(2). 1087–1097. 23 indexed citations
13.
Bordes, Patricia, Sivaramesh Wigneshweraraj, Matthew Chaney, et al.. (2004). Communication between Eσ54, promoter DNA and the conserved threonine residue in the GAFTGA motif of the PspF σ54‐dependent activator during transcription activation. Molecular Microbiology. 54(2). 489–506. 27 indexed citations
14.
Chaney, Matthew, Ricardo Grande, Sivaramesh Wigneshweraraj, et al.. (2001). Binding of transcriptional activators to sigma 54 in the presence of the transition state analog ADP–aluminum fluoride: insights into activator mechanochemical action. Genes & Development. 15(17). 2282–2294. 113 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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