Jane Fridlyand

20.2k total citations · 3 hit papers
68 papers, 10.6k citations indexed

About

Jane Fridlyand is a scholar working on Molecular Biology, Genetics and Cancer Research. According to data from OpenAlex, Jane Fridlyand has authored 68 papers receiving a total of 10.6k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Molecular Biology, 22 papers in Genetics and 17 papers in Cancer Research. Recurrent topics in Jane Fridlyand's work include Genomic variations and chromosomal abnormalities (20 papers), Cancer Genomics and Diagnostics (16 papers) and Gene expression and cancer classification (13 papers). Jane Fridlyand is often cited by papers focused on Genomic variations and chromosomal abnormalities (20 papers), Cancer Genomics and Diagnostics (16 papers) and Gene expression and cancer classification (13 papers). Jane Fridlyand collaborates with scholars based in United States, Netherlands and Canada. Jane Fridlyand's co-authors include Sandrine Dudoit, Terence P. Speed, Boris C. Bastian, Donna G. Albertson, Dan Pinkel, Klaus J. Busam, Toshiro Kageshita, Hetal Patel, Hanni Willenbrock and John A. Curtin and has published in prestigious journals such as Nature, New England Journal of Medicine and Proceedings of the National Academy of Sciences.

In The Last Decade

Jane Fridlyand

66 papers receiving 10.3k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Distinct Sets of Genetic Alterations in Melanoma

2005 1.9k citations John A. Curtin, Jane Fridlyand et al. New England Journal of Medicine profile →
Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data

2002 1.8k citations Sandrine Dudoit, Jane Fridlyand et al. profile →
Widespread potential for growth-factor-driven resistance to anticancer kinase inhibitors

2012 884 citations Timothy R. Wilson, Jane Fridlyand et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jane Fridlyand United States	40	6.7k	3.5k	2.0k	1.4k	1.1k	68	10.6k
Markus Ringnér Sweden	43	7.5k 1.1×	3.0k 0.9×	3.1k 1.5×	1.5k 1.0×	795 0.7×	96	12.2k
Kurt Zatloukal Austria	63	7.1k 1.1×	4.1k 1.2×	2.1k 1.1×	1.9k 1.3×	789 0.7×	287	16.1k
Javed Khan United States	51	6.2k 0.9×	2.2k 0.6×	2.0k 1.0×	905 0.6×	810 0.7×	350	10.9k
Ken Chen United States	49	6.0k 0.9×	2.5k 0.7×	2.9k 1.4×	1.9k 1.4×	564 0.5×	306	11.1k
Mignon L. Loh United States	57	10.1k 1.5×	3.1k 0.9×	1.4k 0.7×	1.0k 0.7×	1.8k 1.7×	317	19.7k
Shridar Ganesan United States	52	7.8k 1.2×	3.5k 1.0×	3.1k 1.5×	1.6k 1.2×	1.6k 1.5×	204	12.7k
Jeffrey R. Marks United States	66	9.3k 1.4×	5.6k 1.6×	4.6k 2.3×	2.5k 1.7×	614 0.6×	222	16.1k
Ethan Dmitrovsky United States	53	11.7k 1.7×	3.0k 0.8×	2.6k 1.3×	2.0k 1.4×	324 0.3×	197	15.2k
Keith Baggerly United States	56	6.8k 1.0×	2.1k 0.6×	2.1k 1.0×	806 0.6×	396 0.4×	157	12.6k
Tatsuhiko Tsunoda Japan	74	9.6k 1.4×	2.5k 0.7×	2.5k 1.2×	2.9k 2.0×	567 0.5×	277	17.6k

Countries citing papers authored by Jane Fridlyand

Since Specialization

Citations

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

Fields of papers citing papers by Jane Fridlyand

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jane Fridlyand

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

All Works

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20 of 20 papers shown

Dejardin, David, Bo Huang, Ying Yuan, et al.. (2024). Dose Optimization for Novel Oncology Agents: Design Options and Strategies. Statistics in Biopharmaceutical Research. 16(3). 326–337. 3 indexed citations

Wilson, Timothy R., Jianjun Yu, Xuyang Lu, et al.. (2016). The molecular landscape of high-risk early breast cancer: comprehensive biomarker analysis of a phase III adjuvant population. npj Breast Cancer. 2(1). 16022–16022. 25 indexed citations

Wilson, Timothy R., Yuanyuan Xiao, Jill M. Spoerke, et al.. (2014). Development of a robust RNA-based classifier to accurately determine ER, PR, and HER2 status in breast cancer clinical samples. Breast Cancer Research and Treatment. 148(2). 315–325. 21 indexed citations

Spoerke, Jill M., Carol O’Brien, Ling Huw, et al.. (2012). Phosphoinositide 3-Kinase (PI3K) Pathway Alterations Are Associated with Histologic Subtypes and Are Predictive of Sensitivity to PI3K Inhibitors in Lung Cancer Preclinical Models. Clinical Cancer Research. 18(24). 6771–6783. 149 indexed citations

Costa, Bruno M., Justin S. Smith, Ying Chen, et al.. (2010). Reversing HOXA9 Oncogene Activation by PI3K Inhibition: Epigenetic Mechanism and Prognostic Significance in Human Glioblastoma. Cancer Research. 70(2). 453–462. 122 indexed citations

Rubenstein, James L., Jane Fridlyand, Lauren E. Abrey, et al.. (2007). Phase I Study of Intraventricular Administration of Rituximab in Patients With Recurrent CNS and Intraocular Lymphoma. Journal of Clinical Oncology. 25(11). 1350–1356. 243 indexed citations

Guan, Yinghui, Wen-Lin Kuo, Jackie L. Stilwell, et al.. (2007). Amplification of PVT1 Contributes to the Pathophysiology of Ovarian and Breast Cancer. Clinical Cancer Research. 13(19). 5745–5755. 295 indexed citations

Climent, Joan, Jane Fridlyand, José Palacios, et al.. (2007). Deletion of Chromosome 11q Predicts Response to Anthracycline-Based Chemotherapy in Early Breast Cancer. Cancer Research. 67(2). 818–826. 67 indexed citations

Fedele, Vita, Jane Fridlyand, Ritu Roydasgupta, et al.. (2007). Mutations in TP53 and associated genomic abnormalities are dependent on breast cancer estrogen receptor status and patient age. Cancer Research. 67. 2962–2962. 1 indexed citations

10.

Yau, Christina, Vita Fedele, Ritu Roydasgupta, et al.. (2007). Aging Impacts Transcriptome but not Genome of Hormone-dependent Breast Cancers. eScholarship (California Digital Library). 1 indexed citations

11.

Terdiman, Jonathan P., Amy J. French, Ritu Roydasgupta, et al.. (2006). Chromosomal Instability in Microsatellite-Unstable and Stable Colon Cancer. Clinical Cancer Research. 12(21). 6379–6385. 73 indexed citations

12.

Chen, Ying, Amanda E. Toland, Jane McLennan, et al.. (2006). Lack of Germ-Line Promoter Methylation in BRCA1 -Negative Families with Familial Breast Cancer. Genetic Testing. 10(4). 281–284. 19 indexed citations

13.

Thorns, Christoph, Boris C. Bastian, Daniel Pinkel, et al.. (2006). Chromosomal aberrations in angioimmunoblastic T‐cell lymphoma and peripheral T‐cell lymphoma unspecified: A matrix‐based CGH approach. Genes Chromosomes and Cancer. 46(1). 37–44. 49 indexed citations

14.

Snijders, Antoine M., Norma J. Nowak, Bing Huey, et al.. (2005). Mapping segmental and sequence variations among laboratory mice using BAC array CGH. Genome Research. 15(2). 302–311. 65 indexed citations

15.

Curtin, John A., Jane Fridlyand, Toshiro Kageshita, et al.. (2005). Distinct Sets of Genetic Alterations in Melanoma. New England Journal of Medicine. 353(20). 2135–2147. 1937 indexed citations breakdown →

16.

Hager, Jeffrey H., John Hodgson, Jane Fridlyand, et al.. (2004). Oncogene Expression and Genetic Background Influence the Frequency of DNA Copy Number Abnormalities in Mouse Pancreatic Islet Cell Carcinomas. Cancer Research. 64(7). 2406–2410. 22 indexed citations

17.

Maldonado, Janet L., Luika Timmerman, Jane Fridlyand, & Boris C. Bastian. (2004). Mechanisms of Cell-Cycle Arrest in Spitz Nevi with Constitutive Activation of the MAP-Kinase Pathway. American Journal Of Pathology. 164(5). 1783–1787. 69 indexed citations

18.

Hackett, Christopher S., John Hodgson, Mark E. Law, et al.. (2003). Genome-wide array CGH analysis of murine neuroblastoma reveals distinct genomic aberrations which parallel those in human tumors.. PubMed. 63(17). 5266–73. 90 indexed citations

19.

Symons, R. C. Andrew, Mark J. Daly, Jane Fridlyand, et al.. (2002). Multiple genetic loci modify susceptibility to plasmacytoma-related morbidity in Eμ-v-abltransgenic mice. Proceedings of the National Academy of Sciences. 99(17). 11299–11304. 25 indexed citations

20.

Dudoit, Sandrine & Jane Fridlyand. (2002). A prediction-based resampling method for estimating the number of clusters in a dataset. Genome biology. 3(7). RESEARCH0036–RESEARCH0036. 487 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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