Bradley M. Broom

58.6k total citations
63 papers, 1.9k citations indexed

About

Bradley M. Broom is a scholar working on Molecular Biology, Cancer Research and Oncology. According to data from OpenAlex, Bradley M. Broom has authored 63 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Molecular Biology, 19 papers in Cancer Research and 14 papers in Oncology. Recurrent topics in Bradley M. Broom's work include Prostate Cancer Treatment and Research (10 papers), Cancer Genomics and Diagnostics (9 papers) and Bioinformatics and Genomic Networks (9 papers). Bradley M. Broom is often cited by papers focused on Prostate Cancer Treatment and Research (10 papers), Cancer Genomics and Diagnostics (9 papers) and Bioinformatics and Genomic Networks (9 papers). Bradley M. Broom collaborates with scholars based in United States, Germany and Australia. Bradley M. Broom's co-authors include Ganiraju C. Manyam, John N. Weinstein, James Melott, Rehan Akbani, Robert Brown, Xiaoping Su, Wing Chung Wong, Michael Ryan, Paul G. Corn and Timothy C. Thompson and has published in prestigious journals such as Nucleic Acids Research, Nature Genetics and Journal of Clinical Oncology.

In The Last Decade

Bradley M. Broom

59 papers receiving 1.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Bradley M. Broom United States 24 1.0k 592 567 474 153 63 1.9k
Sidharth Kumar United States 9 987 1.0× 304 0.5× 461 0.8× 493 1.0× 72 0.5× 35 1.5k
Davide Prandi Italy 16 1.5k 1.5× 520 0.9× 818 1.4× 1.2k 2.6× 83 0.5× 50 2.5k
James F. Reid Italy 19 1.3k 1.3× 325 0.5× 498 0.9× 117 0.2× 98 0.6× 37 1.7k
Pedro Isaacsson Velho United States 18 225 0.2× 394 0.7× 228 0.4× 713 1.5× 63 0.4× 49 993
Alessandro Di Federico Italy 21 375 0.4× 731 1.2× 233 0.4× 488 1.0× 131 0.9× 103 1.4k
Matthew J. Sale United Kingdom 15 918 0.9× 390 0.7× 122 0.2× 108 0.2× 190 1.2× 22 1.2k
Andrea Degasperi United Kingdom 13 746 0.7× 234 0.4× 496 0.9× 124 0.3× 165 1.1× 21 1.3k
Meng‐Feng Tsai Taiwan 21 746 0.7× 856 1.4× 447 0.8× 900 1.9× 91 0.6× 57 1.9k
Satoru Noda Japan 22 492 0.5× 973 1.6× 514 0.9× 321 0.7× 159 1.0× 95 1.7k
Dacheng He China 23 782 0.8× 237 0.4× 181 0.3× 174 0.4× 39 0.3× 60 1.4k

Countries citing papers authored by Bradley M. Broom

Since Specialization
Citations

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

Fields of papers citing papers by Bradley M. Broom

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bradley M. Broom. 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 Bradley M. Broom. The network helps show where Bradley M. Broom may publish in the future.

Co-authorship network of co-authors of Bradley M. Broom

This figure shows the co-authorship network connecting the top 25 collaborators of Bradley M. Broom. A scholar is included among the top collaborators of Bradley M. Broom 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 Bradley M. Broom. Bradley M. Broom 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
1.
Manyam, Ganiraju C., Jody V. Vykoukal, Johannes F. Fahrmann, et al.. (2023). SPOP Mutations Target STING1 Signaling in Prostate Cancer and Create Therapeutic Vulnerabilities to PARP Inhibitor–Induced Growth Suppression. Clinical Cancer Research. 29(21). 4464–4478. 11 indexed citations
2.
Tang, Zhe, Patrick G. Pilié, Chuandong Geng, et al.. (2021). ATR Inhibition Induces CDK1–SPOP Signaling and Enhances Anti–PD-L1 Cytotoxicity in Prostate Cancer. Clinical Cancer Research. 27(17). 4898–4909. 97 indexed citations
3.
Ranasinghe, Weranja, Daniel D. Shapiro, Miao Zhang, et al.. (2021). Optimizing the diagnosis and management of ductal prostate cancer. Nature Reviews Urology. 18(6). 337–358. 22 indexed citations
4.
Volpato, Milène, Michele Cummings, Abeer M. Shaaban, et al.. (2020). Downregulation of 15-hydroxyprostaglandin dehydrogenase during acquired tamoxifen resistance and association with poor prognosis in ERα-positive breast cancer. SHILAP Revista de lepidopterología. 1(5). 355–371. 6 indexed citations
5.
Mokkapati, Sharada, Sima P. Porten, Vikram M. Narayan, et al.. (2020). TCF21 Promotes Luminal-Like Differentiation and Suppresses Metastasis in Bladder Cancer. Molecular Cancer Research. 18(6). 811–821. 6 indexed citations
6.
Menter, David G., Jennifer S. Davis, Bradley M. Broom, et al.. (2019). Back to the Colorectal Cancer Consensus Molecular Subtype Future. Current Gastroenterology Reports. 21(2). 5–5. 54 indexed citations
7.
Su, Xiaoping, Jianping Zhang, Roger Mouawad, et al.. (2017). NSD1 Inactivation and SETD2 Mutation Drive a Convergence toward Loss of Function of H3K36 Writers in Clear Cell Renal Cell Carcinomas. Cancer Research. 77(18). 4835–4845. 38 indexed citations
8.
Broom, Bradley M., Michaël Ryan, Robert Brown, et al.. (2017). A Galaxy Implementation of Next-Generation Clustered Heatmaps for Interactive Exploration of Molecular Profiling Data. Cancer Research. 77(21). e23–e26. 26 indexed citations
9.
Κατσιαμπούρα, Αναστασία, Kanwal Raghav, Zhi-Qin Jiang, et al.. (2017). Modeling of Patient-Derived Xenografts in Colorectal Cancer. Molecular Cancer Therapeutics. 16(7). 1435–1442. 38 indexed citations
10.
Karanika, Styliani, Theodoros Karantanos, Likun Li, et al.. (2017). Targeting DNA Damage Response in Prostate Cancer by Inhibiting Androgen Receptor-CDC6-ATR-Chk1 Signaling. Cell Reports. 18(8). 1970–1981. 64 indexed citations
11.
Davis, Jennifer S., Ruoxi Yu, Zhi-Qin Jiang, et al.. (2017). Distinct Patient and Tumor Characteristics of the Consensus Molecular Subtypes of Colorectal Cancer. Gastroenterology. 152(5). S880–S880. 1 indexed citations
12.
Clarke, Callisia N., Michael S. Lee, Wei Wei, et al.. (2017). Proteomic Features of Colorectal Cancer Identify Tumor Subtypes Independent of Oncogenic Mutations and Independently Predict Relapse-Free Survival. Annals of Surgical Oncology. 24(13). 4051–4058. 27 indexed citations
13.
Aparicio, Ana M., Li Shen, Elsa Li Ning Tapia, et al.. (2015). Combined Tumor Suppressor Defects Characterize Clinically Defined Aggressive Variant Prostate Cancers. Clinical Cancer Research. 22(6). 1520–1530. 192 indexed citations
14.
Gopal, Y.N. Vashisht, Helen Rizos, Guo Chen, et al.. (2014). Inhibition of mTORC1/2 Overcomes Resistance to MAPK Pathway Inhibitors Mediated by PGC1α and Oxidative Phosphorylation in Melanoma. Cancer Research. 74(23). 7037–7047. 148 indexed citations
15.
Ahmed, Ahmed A., Xiaoyan Wang, Zhen Lü, et al.. (2011). Modulating Microtubule Stability Enhances the Cytotoxic Response of Cancer Cells to Paclitaxel. Cancer Research. 71(17). 5806–5817. 45 indexed citations
16.
Broom, Bradley M., Erik P. Sulman, Kim‐Anh Do, Mary E. Edgerton, & Kenneth Aldape. (2010). Bagged gene shaving for the robust clustering of high-throughput data. International Journal of Bioinformatics Research and Applications. 6(4). 326–326. 4 indexed citations
17.
Sanga, Sandeep, Bradley M. Broom, Vittorio Cristini, & Mary E. Edgerton. (2009). Gene expression meta-analysis supports existence of molecular apocrine breast cancer with a role for androgen receptor and implies interactions with ErbB family. BMC Medical Genomics. 2(1). 59–59. 44 indexed citations
18.
Broom, Bradley M., et al.. (2009). Learning robust cell signalling models from high throughput proteomic data. International Journal of Bioinformatics Research and Applications. 5(3). 241–241. 3 indexed citations
19.
Do, K-A, Bradley M. Broom, Petra Kuhnert, et al.. (2000). Genetic analysis of the age at menopause by using estimating equations and Bayesian random effects models. Statistics in Medicine. 19(9). 1217–1235. 53 indexed citations
20.
Ashley, Paul, et al.. (1999). An Implementation of a Secure Version of NFS Including RBAC. Lecture notes in computer science. 31(2). 213–227.

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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