Nancy K. Levin

909 total citations
27 papers, 713 citations indexed

About

Nancy K. Levin is a scholar working on Molecular Biology, Genetics and Oncology. According to data from OpenAlex, Nancy K. Levin has authored 27 papers receiving a total of 713 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 7 papers in Genetics and 6 papers in Oncology. Recurrent topics in Nancy K. Levin's work include Ubiquitin and proteasome pathways (7 papers), Monoclonal and Polyclonal Antibodies Research (6 papers) and BRCA gene mutations in cancer (5 papers). Nancy K. Levin is often cited by papers focused on Ubiquitin and proteasome pathways (7 papers), Monoclonal and Polyclonal Antibodies Research (6 papers) and BRCA gene mutations in cancer (5 papers). Nancy K. Levin collaborates with scholars based in United States, Canada and Australia. Nancy K. Levin's co-authors include Michael A. Tainsky, Judith Abrams, Madhumita Chatterjee, Steven D. Reich, Rouba Ali‐Fehmi, Mohit Trikha, Robert Morris, Suzanne Mellon, Adnan Munkarah and Paul G. Richardson and has published in prestigious journals such as Journal of Clinical Oncology, Blood and PLoS ONE.

In The Last Decade

Nancy K. Levin

27 papers receiving 699 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nancy K. Levin United States 14 327 163 119 113 103 27 713
Marion T. Weigel Germany 16 481 1.5× 338 2.1× 75 0.6× 132 1.2× 113 1.1× 27 1.1k
Mark G. Federici United States 12 201 0.6× 228 1.4× 120 1.0× 120 1.1× 238 2.3× 14 635
Angela Thornton United States 9 322 1.0× 275 1.7× 68 0.6× 33 0.3× 153 1.5× 10 707
Takafumi Koyama Japan 19 293 0.9× 447 2.7× 136 1.1× 34 0.3× 57 0.6× 87 871
Dana M. Roque United States 20 348 1.1× 480 2.9× 165 1.4× 51 0.5× 266 2.6× 61 1.1k
Sadie Whittaker United States 9 258 0.8× 202 1.2× 77 0.6× 69 0.6× 36 0.3× 12 483
Evangelos Bournakis Greece 15 272 0.8× 267 1.6× 39 0.3× 35 0.3× 84 0.8× 35 621
J. H. Doroshow United States 14 187 0.6× 247 1.5× 99 0.8× 44 0.4× 36 0.3× 30 543
Anne Uyei United States 9 303 0.9× 559 3.4× 149 1.3× 113 1.0× 37 0.4× 15 771
Laure Favier France 16 328 1.0× 573 3.5× 193 1.6× 59 0.5× 119 1.2× 61 1.0k

Countries citing papers authored by Nancy K. Levin

Since Specialization
Citations

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

Fields of papers citing papers by Nancy K. Levin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nancy K. Levin. 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 Nancy K. Levin. The network helps show where Nancy K. Levin may publish in the future.

Co-authorship network of co-authors of Nancy K. Levin

This figure shows the co-authorship network connecting the top 25 collaborators of Nancy K. Levin. A scholar is included among the top collaborators of Nancy K. Levin 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 Nancy K. Levin. Nancy K. Levin 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
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Hernandez‐Ilizaliturri, Francisco J., Ian W. Flinn, John Kuruvilla, et al.. (2020). A Phase I Pharmacokinetic (PK) and Safety Study of Trph-222 in Patients with Relapsed/Refractory B-Cell Non-Hodgkin Lymphoma (R/R NHL): Dose-Escalation Results. Blood. 136(Supplement 1). 41–42. 12 indexed citations
3.
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Lopes, Guilherme S., et al.. (2019). FANCM, RAD1, CHEK1 and TP53I3 act as BRCA-like tumor suppressors and are mutated in hereditary ovarian cancer. Cancer Genetics. 235-236. 57–64. 19 indexed citations
5.
Farran, Batoul, Judith Abrams, Michael A. Tainsky, et al.. (2019). Serum folate receptor α (sFR) in ovarian cancer diagnosis and surveillance. Cancer Medicine. 8(3). 920–927. 12 indexed citations
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Bota, Daniela A., Santosh Kesari, David Piccioni, et al.. (2018). A phase 1, multicenter, open-label study of marizomib (MRZ) with temozolomide (TMZ) and radiotherapy (RT) in newly diagnosed WHO grade IV malignant glioma (glioblastoma, ndGBM): Dose-escalation results.. Journal of Clinical Oncology. 36(15_suppl). e14083–e14083. 5 indexed citations
8.
Dyson, Gregory, Nancy K. Levin, Rita Rosati, et al.. (2017). Reanalysis of BRCA1/2 negative high risk ovarian cancer patients reveals novel germline risk loci and insights into missing heritability. PLoS ONE. 12(6). e0178450–e0178450. 34 indexed citations
9.
Chatterjee, Madhumita, et al.. (2017). Utility of paraneoplastic antigens as biomarkers for surveillance and prediction of recurrence in ovarian cancer. Cancer Biomarkers. 20(4). 369–387. 10 indexed citations
10.
Spencer, Andrew, Simon J. Harrison, Jeffrey A. Zonder, et al.. (2017). A phase 1 clinical trial evaluating marizomib, pomalidomide and low‐dose dexamethasone in relapsed and refractory multiple myeloma (NPI‐0052‐107): final study results. British Journal of Haematology. 180(1). 41–51. 60 indexed citations
11.
Wang, Xia, Albert M. Levin, Szymon Smoliński, et al.. (2012). Breast cancer and other neoplasms in women with neurofibromatosis type 1: A retrospective review of cases in the Detroit metropolitan area. American Journal of Medical Genetics Part A. 158A(12). 3061–3064. 39 indexed citations
12.
Fletcher, Nicole M., Zhongliang Jiang, Rouba Ali‐Fehmi, et al.. (2012). Myeloperoxidase and free iron levels: Potential biomarkers for early detection and prognosis of ovarian cancer. Cancer Biomarkers. 10(6). 267–275. 33 indexed citations
13.
Chatterjee, Madhumita, Greg Dyson, Nancy K. Levin, et al.. (2012). Tumor autoantibodies as biomarkers for predicting ovarian cancer recurrence. Cancer Biomarkers. 11(2-3). 59–73. 8 indexed citations
14.
Saed, Ghassan M., Nicole M. Fletcher, Zhong Jiang, et al.. (2011). Abstract 3192: Positive correlation between serum myeloperoxidase and free iron levels with stage of ovarian cancer: Potential biomarkers for early detection and prognosis of ovarian cancer. Cancer Research. 71(8_Supplement). 3192–3192. 3 indexed citations
15.
Ali‐Fehmi, Rouba, Madhumita Chatterjee, Alexei Ionan, et al.. (2009). Analysis of the expression of human tumor antigens in ovarian cancer tissues. Cancer Biomarkers. 6(1). 33–48. 18 indexed citations
16.
Tainsky, Michael A., Madhumita Chatterjee, Nancy K. Levin, Sorin Drăghici, & Judith Abrams. (2007). Multianalyte tests for the early detection of cancer: speedbumps and barriers.. PubMed. 2. 261–7. 2 indexed citations
17.
Mellon, Suzanne, et al.. (2007). Concerns and recommendations regarding inherited cancer risk: The perspectives of survivors and female relatives. Journal of Cancer Education. 22(3). 168–73. 3 indexed citations
18.
Chatterjee, Madhumita, Saroj Kant Mohapatra, Alexei Ionan, et al.. (2006). Diagnostic Markers of Ovarian Cancer by High-Throughput Antigen Cloning and Detection on Arrays. Cancer Research. 66(2). 1181–1190. 165 indexed citations
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Barnholtz‐Sloan, Jill S., Michael A. Tainsky, Judith Abrams, et al.. (2002). Ethnic differences in survival among women with ovarian carcinoma. Cancer. 94(6). 1886–1893. 60 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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