Daniel Hidalgo

1.1k total citations
20 papers, 693 citations indexed

About

Daniel Hidalgo is a scholar working on Physiology, Molecular Biology and Cell Biology. According to data from OpenAlex, Daniel Hidalgo has authored 20 papers receiving a total of 693 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Physiology, 9 papers in Molecular Biology and 4 papers in Cell Biology. Recurrent topics in Daniel Hidalgo's work include Erythrocyte Function and Pathophysiology (10 papers), Genomics and Chromatin Dynamics (4 papers) and RNA Research and Splicing (3 papers). Daniel Hidalgo is often cited by papers focused on Erythrocyte Function and Pathophysiology (10 papers), Genomics and Chromatin Dynamics (4 papers) and RNA Research and Splicing (3 papers). Daniel Hidalgo collaborates with scholars based in United States, Netherlands and Denmark. Daniel Hidalgo's co-authors include Merav Socolovsky, Miroslav Koulnis, Ermelinda Porpiglia, Yung Hwang, Ramona Pop, Ari Waisman, Caleb Weinreb, Jun R. Huh, Rapolas Žilionis and Samuel L. Wolock and has published in prestigious journals such as Nature, Nature Communications and Blood.

In The Last Decade

Daniel Hidalgo

19 papers receiving 690 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 Hidalgo United States 11 411 230 166 148 140 20 693
Raymond T. Doty United States 14 430 1.0× 97 0.4× 231 1.4× 184 1.2× 191 1.4× 27 801
John M. Joslin United States 7 291 0.7× 150 0.7× 199 1.2× 37 0.3× 70 0.5× 10 552
Lee Silverman United States 15 288 0.7× 74 0.3× 107 0.6× 234 1.6× 66 0.5× 26 773
Hemanth Tummala United Kingdom 14 406 1.0× 175 0.8× 63 0.4× 76 0.5× 26 0.2× 35 621
Alexander Groß Germany 8 295 0.7× 55 0.2× 80 0.5× 71 0.5× 40 0.3× 18 445
Nicki Gray United Kingdom 11 533 1.3× 86 0.4× 32 0.2× 74 0.5× 54 0.4× 12 704
Guiqing Huang United States 10 396 1.0× 54 0.2× 68 0.4× 86 0.6× 45 0.3× 14 639
Akinori Kawamura Japan 16 588 1.4× 50 0.2× 120 0.7× 100 0.7× 28 0.2× 41 810
Emery H. Bresnick United States 22 1.5k 3.6× 119 0.5× 276 1.7× 165 1.1× 189 1.4× 26 1.7k
Theodoros Kosteas Greece 10 329 0.8× 104 0.5× 54 0.3× 100 0.7× 62 0.4× 15 555

Countries citing papers authored by Daniel Hidalgo

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Hidalgo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Hidalgo

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Hidalgo. A scholar is included among the top collaborators of Daniel Hidalgo 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 Hidalgo. Daniel Hidalgo 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.
Haines, Eric, Víctor R. De Jesús, Daniel Hidalgo, et al.. (2026). The MEK–RAF molecular glue IK-595 has potent antitumor activity across RAS/MAPK pathway-altered cancers. Nature Cancer. 7(1). 116–130.
2.
Haines, Eric, Víctor R. De Jesús, Daniel Hidalgo, et al.. (2024). Abstract 3296: IK-595, a best-in-class MEK-RAF molecular glue, drives broad and potent anti-tumor activity across RAS-MAPK pathway-altered cancers as a monotherapy and in combination. Cancer Research. 84(6_Supplement). 3296–3296. 2 indexed citations
3.
Hidalgo, Daniel, et al.. (2023). Abstract 3852: IK-930, a paralog-selective novel TEAD-inhibitor, effectively attenuates drug-tolerant persister cell proliferation. Cancer Research. 83(7_Supplement). 3852–3852. 2 indexed citations
4.
5.
Hidalgo, Daniel, Jacob Bejder, Ramona Pop, et al.. (2021). EpoR stimulates rapid cycling and larger red cells during mouse and human erythropoiesis. Nature Communications. 12(1). 7334–7334. 29 indexed citations
6.
7.
Hidalgo, Daniel, Jacob Bejder, Ramona Pop, et al.. (2021). Epor Stimulates Rapid Cycling and Larger Red Cells during Mouse and Human Erythropoiesis. Blood. 138(Supplement 1). 852–852. 3 indexed citations
8.
Oudelaar, A. Marieke, Robert A. Beagrie, Matthew Gosden, et al.. (2020). Dynamics of the 4D genome during in vivo lineage specification and differentiation. Nature Communications. 11(1). 2722–2722. 77 indexed citations
9.
Hwang, Yung, Daniel Hidalgo, & Merav Socolovsky. (2020). The shifting shape and functional specializations of the cell cycle during lineage development. PubMed. 13(2). e1504–e1504. 11 indexed citations
10.
Tusi, Betsabeh Khoramian, Samuel L. Wolock, Caleb Weinreb, et al.. (2018). Population snapshots predict early haematopoietic and erythroid hierarchies. Nature. 555(7694). 54–60. 244 indexed citations
11.
Stone, Nicholas P., Brendan J. Hilbert, Daniel Hidalgo, et al.. (2018). A Hyperthermophilic Phage Decoration Protein Suggests Common Evolutionary Origin with Herpesvirus Triplex Proteins and an Anti-CRISPR Protein. Structure. 26(7). 936–947.e3. 19 indexed citations
12.
Hwang, Yung, Daniel Hidalgo, Ramona Pop, et al.. (2017). Global increase in replication fork speed during a p57 KIP2 -regulated erythroid cell fate switch. Science Advances. 3(5). e1700298–e1700298. 40 indexed citations
13.
Hidalgo, Daniel, et al.. (2017). LISA: Lexically Intelligent Story Assistant. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 13(1). 221–227. 5 indexed citations
14.
Socolovsky, Merav, et al.. (2017). Global increase in replication fork speed during a p57KIP2-regulated erythroid cell fate switch. Experimental Hematology. 53. S29–S29. 1 indexed citations
15.
Hwang, Yung, et al.. (2016). Global Increase in Replication Fork Speed during a p57KIP2-Regulated Erythroid Cell Fate Switch. Blood. 128(22). 698–698. 4 indexed citations
16.
Koulnis, Miroslav, Ermelinda Porpiglia, Daniel Hidalgo, & Merav Socolovsky. (2014). Erythropoiesis: From Molecular Pathways to System Properties. Advances in experimental medicine and biology. 844. 37–58. 32 indexed citations
17.
Porpiglia, Ermelinda, Daniel Hidalgo, Miroslav Koulnis, Abraham R. Tzafriri, & Merav Socolovsky. (2012). Stat5 Signaling Specifies Basal versus Stress Erythropoietic Responses through Distinct Binary and Graded Dynamic Modalities. PLoS Biology. 10(8). e1001383–e1001383. 34 indexed citations
18.
Koulnis, Miroslav, Ramona Pop, Ermelinda Porpiglia, et al.. (2011). Identification and Analysis of Mouse Erythroid Progenitors using the CD71/TER119 Flow-cytometric Assay. Journal of Visualized Experiments. 108 indexed citations
19.
Koulnis, Miroslav, Ermelinda Porpiglia, Ying Liu, et al.. (2011). Contrasting dynamic responses in vivo of the Bcl-xL and Bim erythropoietic survival pathways. Blood. 119(5). 1228–1239. 37 indexed citations
20.
Koulnis, Miroslav, Ramona Pop, Ermelinda Porpiglia, et al.. (2011). Identification and Analysis of Mouse Erythroid Progenitors using the CD71/TER119 Flow-cytometric Assay. Journal of Visualized Experiments. 38 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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