Marilyn A. Owens

2.3k total citations · 2 hit papers
18 papers, 1.8k citations indexed

About

Marilyn A. Owens is a scholar working on Molecular Biology, Cancer Research and Pathology and Forensic Medicine. According to data from OpenAlex, Marilyn A. Owens has authored 18 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 8 papers in Cancer Research and 4 papers in Pathology and Forensic Medicine. Recurrent topics in Marilyn A. Owens's work include Cancer Genomics and Diagnostics (6 papers), Breast Cancer Treatment Studies (5 papers) and Single-cell and spatial transcriptomics (4 papers). Marilyn A. Owens is often cited by papers focused on Cancer Genomics and Diagnostics (6 papers), Breast Cancer Treatment Studies (5 papers) and Single-cell and spatial transcriptomics (4 papers). Marilyn A. Owens collaborates with scholars based in United States. Marilyn A. Owens's co-authors include Gary M. Clark, Bruce Horten, William McGuire, Lynn G. Dressler, Teri Oldaker, George Pounds, Larry C. Seamer, M.R. Pandian, L. G. Dressler and Charlotte R. Wenger and has published in prestigious journals such as New England Journal of Medicine, Journal of Clinical Oncology and Cancer.

In The Last Decade

Marilyn A. Owens

18 papers receiving 1.8k citations

Hit Papers

HER2 Amplification Ratios by Fluorescence In Situ H... 1989 2026 2001 2013 2004 1989 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marilyn A. Owens United States 14 1.0k 900 547 357 356 18 1.8k
Ellen C.M. Mommers Netherlands 16 815 0.8× 805 0.9× 700 1.3× 240 0.7× 341 1.0× 19 1.8k
Melora D. Berardo United States 8 1.3k 1.3× 962 1.1× 811 1.5× 364 1.0× 246 0.7× 10 2.4k
S Pinder United Kingdom 23 670 0.6× 725 0.8× 386 0.7× 552 1.5× 261 0.7× 62 1.6k
H D Sinnett United Kingdom 20 1.1k 1.0× 986 1.1× 432 0.8× 472 1.3× 194 0.5× 44 2.0k
Carl‐Magnus Rudenstam Sweden 20 1.7k 1.7× 954 1.1× 439 0.8× 488 1.4× 511 1.4× 35 2.5k
Tracy Lively United States 10 991 1.0× 1.2k 1.4× 536 1.0× 411 1.2× 251 0.7× 19 2.1k
Marie‐Christine Mathieu France 25 978 0.9× 705 0.8× 461 0.8× 445 1.2× 415 1.2× 63 2.1k
W Richardson United States 6 1.5k 1.5× 1.5k 1.6× 733 1.3× 756 2.1× 427 1.2× 9 2.9k
Craig Allred United States 16 1.7k 1.7× 1.5k 1.6× 996 1.8× 528 1.5× 383 1.1× 19 3.1k
J Sugár Hungary 14 765 0.7× 536 0.6× 422 0.8× 378 1.1× 196 0.6× 74 1.8k

Countries citing papers authored by Marilyn A. Owens

Since Specialization
Citations

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

Fields of papers citing papers by Marilyn A. Owens

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marilyn A. Owens. 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 Marilyn A. Owens. The network helps show where Marilyn A. Owens may publish in the future.

Co-authorship network of co-authors of Marilyn A. Owens

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

All Works

18 of 18 papers shown
1.
Owens, Marilyn A., et al.. (2004). HER2 Amplification Ratios by Fluorescence In Situ Hybridization and Correlation with Immunohistochemistry in a Cohort of 6556 Breast Cancer Tissues. Clinical Breast Cancer. 5(1). 63–69. 510 indexed citations breakdown →
2.
Choppa, Paul, et al.. (2003). A Novel Method for the Detection, Quantitation, and Breakpoint Cluster Region Determination of t(15;17) Fusion Transcripts Using a One-Step Real-Time Multiplex RT-PCR. American Journal of Clinical Pathology. 119(1). 137–144. 13 indexed citations
3.
Choppa, Paul, et al.. (2003). A Novel Method for the Detection, Quantitation, and Breakpoint Cluster Region Determination of t(15;17) Fusion Transcripts Using a One-Step Real-Time Multiplex RT-PCR. American Journal of Clinical Pathology. 119(1). 137–144. 13 indexed citations
4.
Owens, Marilyn A., et al.. (2000). Validation and quality control of immunophenotyping in clinical flow cytometry. Journal of Immunological Methods. 243(1-2). 33–50. 72 indexed citations
5.
Loken, Michael R. & Marilyn A. Owens. (1994). Flow Cytometry Principles for Clinical Laboratory Practice: Quality Assurance for Quantitative Immunophenotyping. Medical Entomology and Zoology. 27 indexed citations
6.
Ravdin, Peter M., et al.. (1993). Neural network analysis of DNA flow cytometry histograms. Cytometry. 14(1). 74–80. 21 indexed citations
7.
Clark, Gary M., Charlotte R. Wenger, Marilyn A. Owens, et al.. (1993). How to integrate steroid hormone receptor, flow cytometric, and other prognostic information in regard to primary breast cancer. Cancer. 71(S6). 2157–2162. 65 indexed citations
8.
Wenger, Charlotte R., Marilyn A. Owens, George Pounds, et al.. (1993). DNA ploidy, S-phase, and steroid receptors in more than 127,000 breast cancer patients. Breast Cancer Research and Treatment. 28(1). 9–20. 123 indexed citations
9.
Ravdin, Peter M., Gary M. Clark, Susan G. Hilsenbeck, et al.. (1992). A demonstration that breast cancer recurrence can be predicted by Neural Network analysis. Breast Cancer Research and Treatment. 21(1). 47–53. 63 indexed citations
10.
Clark, Gary M., M. Mathieu, Marilyn A. Owens, et al.. (1992). Prognostic significance of S-phase fraction in good-risk, node-negative breast cancer patients.. Journal of Clinical Oncology. 10(3). 428–432. 97 indexed citations
11.
McGuckin, Michael A., et al.. (1990). Demonstration of seven tumor-associated antigens in epithelial ovarian cancer by immunohistochemistry using monoclonal antibodies. 5(1). 87–94. 5 indexed citations
12.
Rosen, Clifford J., et al.. (1990). T lymphocyte surface antigen markers in osteoporosis. Journal of Bone and Mineral Research. 5(8). 851–855. 24 indexed citations
13.
Clark, Gary M., Lynn G. Dressler, Marilyn A. Owens, et al.. (1989). Prediction of Relapse or Survival in Patients with Node-Negative Breast Cancer by DNA Flow Cytometry. New England Journal of Medicine. 320(10). 627–633. 466 indexed citations breakdown →
14.
Dressler, Lynn G., Larry C. Seamer, Marilyn A. Owens, Gary M. Clark, & William McGuire. (1988). DNA flow cytometry and prognostic factors in 1331 frozen breast cancer specimens. Cancer. 61(3). 420–427. 254 indexed citations
15.
Owens, Marilyn A.. (1988). Role of the laboratory in clinical flow cytometry: Present and future. Cytometry. 9(S3). 101–103. 2 indexed citations
16.
Dressler, L. G., et al.. (1987). Evaluation of a modeling system for S-phase estimation in breast cancer by flow cytometry.. PubMed. 47(20). 5294–302. 62 indexed citations
17.
Berk, Lee, et al.. (1986). Lymphocyte Subset Changes During Acute Maximal Exercise. Medicine & Science in Sports & Exercise. 18(6). 706–706. 26 indexed citations
18.
Owens, Marilyn A.. (1972). Agriculture group symposium 23rd November 1971. Intensive animal production as a source of pollution. Journal of the Science of Food and Agriculture. 23(6). 793–796. 1 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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