Todd Hricik

6.5k total citations · 1 hit paper
9 papers, 2.5k citations indexed

About

Todd Hricik is a scholar working on Molecular Biology, Hematology and Genetics. According to data from OpenAlex, Todd Hricik has authored 9 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 4 papers in Hematology and 3 papers in Genetics. Recurrent topics in Todd Hricik's work include Myeloproliferative Neoplasms: Diagnosis and Treatment (3 papers), Acute Myeloid Leukemia Research (3 papers) and Epigenetics and DNA Methylation (2 papers). Todd Hricik is often cited by papers focused on Myeloproliferative Neoplasms: Diagnosis and Treatment (3 papers), Acute Myeloid Leukemia Research (3 papers) and Epigenetics and DNA Methylation (2 papers). Todd Hricik collaborates with scholars based in United States, Australia and Malaysia. Todd Hricik's co-authors include Stuart Bevan, Johannes Mosbacher, Andrea Peier, Alison J. Reeve, Ardem Patapoutian, Taryn J. Earley, Samer R. Eid, Timothy Jegla, Peter McIntyre and Anne C. Hergarden and has published in prestigious journals such as Cell, Blood and Molecular Cancer Therapeutics.

In The Last Decade

Todd Hricik

9 papers receiving 2.5k citations

Hit Papers

ANKTM1, a TRP-like Channel Expressed in Nociceptive Neuro... 2003 2026 2010 2018 2003 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
Todd Hricik United States 8 1.7k 781 667 593 410 9 2.5k
Kazuya Togashi Japan 10 780 0.5× 332 0.4× 446 0.7× 399 0.7× 35 0.1× 15 1.4k
Tarik Smani Spain 30 1.1k 0.7× 585 0.7× 389 0.6× 1.2k 2.0× 26 0.1× 96 2.6k
Maarten Gees Belgium 16 964 0.6× 327 0.4× 349 0.5× 415 0.7× 26 0.1× 24 1.5k
Katharine Walker United States 15 439 0.3× 360 0.5× 768 1.2× 375 0.6× 141 0.3× 18 1.6k
Sangsu Bang United States 25 669 0.4× 598 0.8× 941 1.4× 607 1.0× 24 0.1× 39 2.4k
Claudia Trost Germany 13 1.4k 0.9× 699 0.9× 178 0.3× 1.1k 1.9× 18 0.0× 17 2.1k
Manabu Murakami Japan 26 877 0.5× 593 0.8× 340 0.5× 1.2k 2.0× 26 0.1× 90 2.3k
Yoko Fujii Japan 20 595 0.4× 334 0.4× 184 0.3× 657 1.1× 29 0.1× 58 1.7k
Wayne I. DeHaven United States 16 2.0k 1.2× 982 1.3× 239 0.4× 1.2k 1.9× 27 0.1× 18 2.7k
Clemens Gillen Germany 28 611 0.4× 862 1.1× 610 0.9× 821 1.4× 31 0.1× 44 2.4k

Countries citing papers authored by Todd Hricik

Since Specialization
Citations

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

Fields of papers citing papers by Todd Hricik

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Todd Hricik

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

All Works

9 of 9 papers shown
1.
Hricik, Todd, David A. Bader, & Oded Green. (2020). Using RAPIDS AI to Accelerate Graph Data Science Workflows. 1–4. 8 indexed citations
2.
Perna, Fabiana, Ly Vu, Maria Themeli, et al.. (2015). The Polycomb Group Protein L3MBTL1 Represses a SMAD5-Mediated Hematopoietic Transcriptional Program in Human Pluripotent Stem Cells. Stem Cell Reports. 4(4). 658–669. 5 indexed citations
3.
Rampal, Raajit K., Fátima Al‐Shahrour, Omar Abdel‐Wahab, et al.. (2014). Integrated genomic analysis illustrates the central role of JAK-STAT pathway activation in myeloproliferative neoplasm pathogenesis. Blood. 123(22). e123–e133. 287 indexed citations
4.
Hricik, Todd, G. Federici, Ann Zeuner, et al.. (2013). Transcriptomic and phospho‐proteomic analyzes of erythroblasts expanded in vitro from normal donors and from patients with polycythemia vera. American Journal of Hematology. 88(9). 723–729. 16 indexed citations
5.
Rampal, Raajit K., Taghi Manshouri, Jay Patel, et al.. (2012). Genetic analysis of patients with leukemic transformation of myeloproliferative neoplasms shows recurrent SRSF2 mutations that are associated with adverse outcome. Blood. 119(19). 4480–4485. 161 indexed citations
6.
Mills, Joslyn, Todd Hricik, Sara Siddiqi, & Igor Matushansky. (2011). Chromatin Structure Predicts Epigenetic Therapy Responsiveness in Sarcoma. Molecular Cancer Therapeutics. 10(2). 313–324. 10 indexed citations
7.
Charytonowicz, Elizabeth, et al.. (2011). Alternate PAX3 and PAX7 C-terminal isoforms in myogenic differentiation and sarcomagenesis. Clinical & Translational Oncology. 13(3). 194–203. 11 indexed citations
8.
Mills, Joslyn, Tulio Matos, Elizabeth Charytonowicz, et al.. (2009). Characterization and comparison of the properties of sarcoma cell linesin vitroandin vivo. Human Cell. 22(4). 85–93. 19 indexed citations
9.
Story, Gina M., Andrea Peier, Alison J. Reeve, et al.. (2003). ANKTM1, a TRP-like Channel Expressed in Nociceptive Neurons, Is Activated by Cold Temperatures. Cell. 112(6). 819–829. 1990 indexed citations breakdown →

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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