Gene Cutler

1.5k total citations
18 papers, 1.1k citations indexed

About

Gene Cutler is a scholar working on Molecular Biology, Pulmonary and Respiratory Medicine and Pathology and Forensic Medicine. According to data from OpenAlex, Gene Cutler has authored 18 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 3 papers in Pulmonary and Respiratory Medicine and 2 papers in Pathology and Forensic Medicine. Recurrent topics in Gene Cutler's work include RNA and protein synthesis mechanisms (3 papers), Machine Learning in Bioinformatics (2 papers) and Genomic variations and chromosomal abnormalities (2 papers). Gene Cutler is often cited by papers focused on RNA and protein synthesis mechanisms (3 papers), Machine Learning in Bioinformatics (2 papers) and Genomic variations and chromosomal abnormalities (2 papers). Gene Cutler collaborates with scholars based in United States, South Africa and China. Gene Cutler's co-authors include Robert Tjian, James A. Goodrich, Timothy Hoey, Paul D. Kassner, Lisa A. Marshall, Hui Tian, Jinlong Chen, Xuefeng B. Ling, Zhibin Pan and Hélène Baribault and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Blood.

In The Last Decade

Gene Cutler

18 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Gene Cutler United States	13	598	167	148	141	129	18	1.1k
Hadara Rubinfeld Israel	18	780 1.3×	226 1.4×	93 0.6×	60 0.4×	89 0.7×	27	1.3k
Laura R. Pearce United Kingdom	8	1.6k 2.7×	188 1.1×	162 1.1×	98 0.7×	167 1.3×	9	2.1k
Jean Labrecque Canada	16	639 1.1×	368 2.2×	298 2.0×	179 1.3×	63 0.5×	34	1.2k
Noriko Yasuhara Japan	15	936 1.6×	103 0.6×	127 0.9×	56 0.4×	91 0.7×	29	1.2k
Bijay S. Jaiswal United States	19	625 1.0×	155 0.9×	168 1.1×	26 0.2×	131 1.0×	26	1.3k
Stefan Legewie Germany	23	1.3k 2.2×	143 0.9×	87 0.6×	44 0.3×	107 0.8×	44	1.6k
Roxane Desjardins Canada	18	543 0.9×	177 1.1×	179 1.2×	36 0.3×	75 0.6×	37	1.1k
Mohammad Fallahi United States	26	1.2k 2.1×	264 1.6×	281 1.9×	47 0.3×	118 0.9×	41	1.9k
Kimberley A. Beaumont Australia	16	553 0.9×	288 1.7×	132 0.9×	64 0.5×	44 0.3×	23	1.1k
Ximena Opitz-Araya United States	8	1.4k 2.4×	383 2.3×	234 1.6×	274 1.9×	88 0.7×	8	2.0k

Countries citing papers authored by Gene Cutler

Since Specialization

Citations

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

Fields of papers citing papers by Gene Cutler

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gene Cutler

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

All Works

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18 of 18 papers shown

Jorapur, Aparna, Lisa A. Marshall, Mengshu Xu, et al.. (2022). EBV+ tumors exploit tumor cell-intrinsic and -extrinsic mechanisms to produce regulatory T cell-recruiting chemokines CCL17 and CCL22. PLoS Pathogens. 18(1). e1010200–e1010200. 19 indexed citations

Marshall, Lisa A., Sachie Marubayashi, Aparna Jorapur, et al.. (2020). Tumors establish resistance to immunotherapy by regulating T_regrecruitment via CCR4. Journal for ImmunoTherapy of Cancer. 8(2). e000764–e000764. 91 indexed citations

Cutler, Gene & Jordan S. Fridman. (2016). A Machine-Learning Analysis Suggests That FLX925, a FLT3/CDK4/6 Kinase Inhibitor, Is Potent Against FLT3-Wild Type Tumors Via Its CDK4/6 Activity. Blood. 128(22). 3520–3520. 3 indexed citations

Marubayashi, Sachie, Adam Park, Rajkumar Noubade, et al.. (2016). FLX925 Is a Rationally Designed FLT3, CDK4/6 Inhibitor with a Desirable Resistance Profile. Blood. 128(22). 2323–2323. 3 indexed citations

Baribault, Hélène, Hongfei Ge, Jinghong Wang, et al.. (2014). Advancing therapeutic discovery through phenotypic screening of the extracellular proteome using hydrodynamic intravascular injection. Expert Opinion on Therapeutic Targets. 18(11). 1253–1264. 2 indexed citations

Blum, Roy, Patricia E. Burger, Christopher S. Ontiveros, et al.. (2010). Molecular Signatures of the Primitive Prostate Stem Cell Niche Reveal Novel Mesenchymal-Epithelial Signaling Pathways. PLoS ONE. 5(9). e13024–e13024. 19 indexed citations

Tchaparian, Eskouhie, Gene Cutler, Kathryn Bauerly, et al.. (2010). Identification of transcriptional networks responding to pyrroloquinoline quinone dietary supplementation and their influence on thioredoxin expression, and the JAK/STAT and MAPK pathways. Biochemical Journal. 429(3). 515–526. 39 indexed citations

Blum, Roy, Rashmi Gupta, Patricia E. Burger, et al.. (2009). Molecular Signatures of Prostate Stem Cells Reveal Novel Signaling Pathways and Provide Insights into Prostate Cancer. PLoS ONE. 4(5). e5722–e5722. 54 indexed citations

Cutler, Gene & Paul D. Kassner. (2008). Copy number variation in the mouse genome: implications for the mouse as a model organism for human disease. Cytogenetic and Genome Research. 123(1-4). 297–306. 14 indexed citations

10.

Cutler, Gene, et al.. (2007). Significant gene content variation characterizes the genomes of inbred mouse strains. Genome Research. 17(12). 1743–1754. 83 indexed citations

11.

Liu, Jason J., Gene Cutler, Wenyuan Li, et al.. (2005). Multiclass cancer classification and biomarker discovery using GA-based algorithms. Computer applications in the biosciences. 21(11). 2691–2697. 132 indexed citations

12.

Herter, Sylvia, Derek E. Piper, Wade H. Aaron, et al.. (2005). Hepatocyte growth factor is a preferred in vitro substrate for human hepsin, a membrane-anchored serine protease implicated in prostate and ovarian cancers. Biochemical Journal. 390(1). 125–136. 152 indexed citations

13.

Gupte, Jamila, Gene Cutler, Jinlong Chen, & Hui Tian. (2004). Elucidation of signaling properties of vasopressin receptor-related receptor 1 by using the chimeric receptor approach. Proceedings of the National Academy of Sciences. 101(6). 1508–1513. 42 indexed citations

14.

Li, Shuyu, Gene Cutler, Jane Jijun Liu, et al.. (2003). A comparative analysis of HGSC and Celera human genome assemblies and gene sets. Bioinformatics. 19(13). 1597–1605. 7 indexed citations

15.

Li, Shuyu, Jiayu Liao, Gene Cutler, et al.. (2002). Comparative Analysis of Human Genome Assemblies Reveals Genome-Level Differences. Genomics. 80(2). 138–139. 8 indexed citations

16.

An, Songzhu, Gene Cutler, Jack Zhao, et al.. (2001). Identification and characterization of a melanin-concentrating hormone receptor. Proceedings of the National Academy of Sciences. 98(13). 7576–7581. 161 indexed citations

17.

Cutler, Gene, et al.. (1998). Adf-1 Is a Nonmodular Transcription Factor That Contains a TAF-Binding Myb-Like Motif. Molecular and Cellular Biology. 18(4). 2252–2261. 43 indexed citations

18.

Goodrich, James A., Gene Cutler, & Robert Tjian. (1996). Contacts in Context: Promoter Specificity and Macromolecular Interactions in Transcription. Cell. 84(6). 825–830. 183 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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