John Curfman

1.7k total citations
10 papers, 608 citations indexed

About

John Curfman is a scholar working on Molecular Biology, Hematology and Genetics. According to data from OpenAlex, John Curfman has authored 10 papers receiving a total of 608 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 3 papers in Hematology and 2 papers in Genetics. Recurrent topics in John Curfman's work include Epigenetics and DNA Methylation (6 papers), RNA modifications and cancer (5 papers) and Acute Myeloid Leukemia Research (3 papers). John Curfman is often cited by papers focused on Epigenetics and DNA Methylation (6 papers), RNA modifications and cancer (5 papers) and Acute Myeloid Leukemia Research (3 papers). John Curfman collaborates with scholars based in United States, Japan and Taiwan. John Curfman's co-authors include Hai‐Ying Mary Cheng, Heather Dziema, Takanobu Nakazawa, Soren Impey, Brandon S Russell, Kimiko Shimizu, Olga Varlamova, Karl Obrietan, Hitoshi Okamura and William Blum and has published in prestigious journals such as Neuron, Journal of Clinical Oncology and Blood.

In The Last Decade

John Curfman

9 papers receiving 596 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John Curfman United States 6 309 228 205 94 73 10 608
Maud Demarque France 5 148 0.5× 26 0.1× 198 1.0× 36 0.4× 137 1.9× 5 392
Lale Dawut United States 12 785 2.5× 186 0.8× 314 1.5× 560 6.0× 291 4.0× 12 1.2k
Debra Brooker United Kingdom 5 142 0.5× 15 0.1× 337 1.6× 171 1.8× 101 1.4× 5 532
Frank M. J. Jacobs Netherlands 14 1.2k 3.8× 139 0.6× 16 0.1× 269 2.9× 102 1.4× 19 1.4k
Wangjie Yu United States 17 329 1.1× 107 0.5× 750 3.7× 483 5.1× 148 2.0× 21 1.1k
Matías Alvarez-Saavedra Canada 10 282 0.9× 85 0.4× 120 0.6× 46 0.5× 48 0.7× 13 503
Zhixiong Ma China 7 129 0.4× 78 0.3× 221 1.1× 28 0.3× 143 2.0× 13 415
Laurie R. Earls United States 9 376 1.2× 93 0.4× 12 0.1× 10 0.1× 31 0.4× 12 488
W. Brysch Germany 11 285 0.9× 24 0.1× 138 0.7× 28 0.3× 66 0.9× 14 570
Benno Schindelholz Switzerland 10 512 1.7× 27 0.1× 63 0.3× 26 0.3× 98 1.3× 10 735

Countries citing papers authored by John Curfman

Since Specialization
Citations

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

Fields of papers citing papers by John Curfman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of John Curfman

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

All Works

10 of 10 papers shown
1.
Oman, Kenji, et al.. (2016). Characterization of DNA-protein interactions using high-throughput sequencing data from pulldown experiments. Bulletin of the American Physical Society. 2016.
2.
Oman, Kenji, et al.. (2016). Methyl-CpG/MBD2 Interaction Requires Minimum Separation and Exhibits Minimal Sequence Specificity. Biophysical Journal. 111(12). 2551–2561. 5 indexed citations
3.
Frankhouser, David, Mark W. Murphy, James S. Blachly, et al.. (2014). PrEMeR-CG: inferring nucleotide level DNA methylation values from MethylCap-seq data. Bioinformatics. 30(24). 3567–3574. 6 indexed citations
4.
Rodríguez, Blanca, David Frankhouser, Mark W. Murphy, et al.. (2012). Methods for high-throughput MethylCap-Seq data analysis. BMC Genomics. 13(S6). S14–S14. 25 indexed citations
5.
Trimarchi, Michael P., Mark W. Murphy, David Frankhouser, et al.. (2012). Enrichment-based DNA methylation analysis using next-generation sequencing: sample exclusion, estimating changes in global methylation, and the contribution of replicate lanes. BMC Genomics. 13(S8). S6–S6. 11 indexed citations
6.
Blum, William, Rebecca B. Klisovic, Alison Walker, et al.. (2012). Priming of Mir-181a in Acute Myeloid Leukemia (AML) to Increase Chemosensitivity: A Phase I Trial of Lenalidomide (LEN) Followed by Idarubicin and Cytarabine.. Blood. 120(21). 2619–2619. 1 indexed citations
7.
Rodríguez, Blanca, David Frankhouser, Michael P. Trimarchi, et al.. (2011). A scalable, flexible workflow for MethylCap-seq data analysis. PubMed. 1–4. 5 indexed citations
8.
Walker, Alison R., Klaus H. Metzeler, Susan M. Geyer, et al.. (2011). Impact of DNMT3A mutations on Clinical Response to the Hypomethylating Agent Decitabine in Older Patients (pts) with Acute Myeloid Leukemia (AML). Blood. 118(21). 944–944. 2 indexed citations
9.
Blum, William, Rebecca B. Klisovic, Heiko Becker, et al.. (2010). Dose Escalation of Lenalidomide in Relapsed or Refractory Acute Leukemias. Journal of Clinical Oncology. 28(33). 4919–4925. 71 indexed citations
10.
Cheng, Hai‐Ying Mary, Olga Varlamova, Heather Dziema, et al.. (2007). microRNA Modulation of Circadian-Clock Period and Entrainment. Neuron. 54(5). 813–829. 482 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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