Brian T. Golitz

587 total citations
16 papers, 276 citations indexed

About

Brian T. Golitz is a scholar working on Molecular Biology, Oncology and Cancer Research. According to data from OpenAlex, Brian T. Golitz has authored 16 papers receiving a total of 276 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 6 papers in Oncology and 4 papers in Cancer Research. Recurrent topics in Brian T. Golitz's work include Computational Drug Discovery Methods (3 papers), Protein Degradation and Inhibitors (3 papers) and Melanoma and MAPK Pathways (3 papers). Brian T. Golitz is often cited by papers focused on Computational Drug Discovery Methods (3 papers), Protein Degradation and Inhibitors (3 papers) and Melanoma and MAPK Pathways (3 papers). Brian T. Golitz collaborates with scholars based in United States and Germany. Brian T. Golitz's co-authors include Gary L. Johnson, Jon S. Zawistowski, Noah Sciaky, Joel S. Parker, Deborah A. Granger, Kazuhiro Nakamura, Naim U. Rashid, Vijay Swahari, Olga Karginova and Yolanda Y. Huang and has published in prestigious journals such as Molecular and Cellular Biology, Cancer Research and The FASEB Journal.

In The Last Decade

Brian T. Golitz

13 papers receiving 276 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Brian T. Golitz United States	7	213	77	59	28	23	16	276
H. Nikki March United Kingdom	5	228 1.1×	83 1.1×	80 1.4×	33 1.2×	30 1.3×	5	296
Anna A. Marusiak Poland	9	192 0.9×	82 1.1×	70 1.2×	43 1.5×	17 0.7×	16	256
Cristina Zahonero Spain	6	174 0.8×	66 0.9×	55 0.9×	19 0.7×	28 1.2×	7	318
G. Tjitske Los-de Vries Netherlands	3	187 0.9×	86 1.1×	62 1.1×	63 2.3×	35 1.5×	5	277
Weijie Song China	8	192 0.9×	88 1.1×	80 1.4×	19 0.7×	38 1.7×	11	274
Jingying Cao China	7	237 1.1×	68 0.9×	68 1.2×	13 0.5×	16 0.7×	11	289
Alexander E. Kudinov United States	5	235 1.1×	70 0.9×	94 1.6×	17 0.6×	24 1.0×	6	314
Sandrine Agoussi France	4	254 1.2×	104 1.4×	27 0.5×	25 0.9×	46 2.0×	4	314
Mike Berger United States	2	284 1.3×	53 0.7×	40 0.7×	45 1.6×	27 1.2×	3	344
Zandra V. Ho United States	4	216 1.0×	43 0.6×	70 1.2×	14 0.5×	12 0.5×	4	256

Countries citing papers authored by Brian T. Golitz

Since Specialization

Citations

This map shows the geographic impact of Brian T. Golitz'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 Brian T. Golitz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian T. Golitz more than expected).

Fields of papers citing papers by Brian T. Golitz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Brian T. Golitz. 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 Brian T. Golitz. The network helps show where Brian T. Golitz may publish in the future.

Co-authorship network of co-authors of Brian T. Golitz

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

All Works

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16 of 16 papers shown

Berginski, Matthew E., Brian T. Golitz, Matthew B. Lipner, et al.. (2024). Kinome state is predictive of cell viability in pancreatic cancer tumor and cancer-associated fibroblast cell lines. PeerJ. 12. e17797–e17797.

Bevill, Samantha M., Noah Sciaky, Brian T. Golitz, et al.. (2024). Neratinib, a pan ERBB/HER inhibitor, restores sensitivity of PTEN-null, BRAFV600E melanoma to BRAF/MEK inhibition. Frontiers in Oncology. 14. 1191217–1191217. 3 indexed citations

Pearce, Kenneth H., et al.. (2024). A First-in-Class High-Throughput Screen to Discover Modulators of the Alternative Lengthening of Telomeres (ALT) Pathway. ACS Pharmacology & Translational Science. 7(9). 2799–2819. 1 indexed citations

Berginski, Matthew E., et al.. (2023). Integrated single-dose kinome profiling data is predictive of cancer cell line sensitivity to kinase inhibitors. PeerJ. 11. e16342–e16342. 1 indexed citations

Berginski, Matthew E., et al.. (2023). Kinome inhibition states and multiomics data enable prediction of cell viability in diverse cancer types. PLoS Computational Biology. 19(2). e1010888–e1010888. 1 indexed citations

Lipner, Matthew B., Xianlu L. Peng, Chong Jin, et al.. (2020). Irreversible JNK1-JUN inhibition by JNK-IN-8 sensitizes pancreatic cancer to 5-FU/FOLFOX chemotherapy. JCI Insight. 5(8). 16–18. 24 indexed citations

Bevill, Samantha M., Noah Sciaky, Brian T. Golitz, et al.. (2019). GSK2801, a BAZ2/BRD9 Bromodomain Inhibitor, Synergizes with BET Inhibitors to Induce Apoptosis in Triple-Negative Breast Cancer. Molecular Cancer Research. 17(7). 1503–1518. 44 indexed citations

Bevill, Samantha M., Noah Sciaky, Brian T. Golitz, et al.. (2018). Abstract B34: Novel synergistic combination therapies with BET bromodomain inhibitors in triple-negative breast cancer. Molecular Cancer Research. 16(8_Supplement). B34–B34. 1 indexed citations

Lee, Benjamin, Michael P. East, Thomas S.K. Gilbert, et al.. (2018). Application of Integrated Drug Screening/Kinome Analysis to Identify Inhibitors of Gemcitabine-Resistant Pancreatic Cancer Cell Growth. SLAS DISCOVERY. 23(8). 850–861. 13 indexed citations

10.

Swearingen, Amanda E.D. Van, Maria J. Sambade, Marni B. Siegel, et al.. (2017). Combined kinase inhibitors of MEK1/2 and either PI3K or PDGFR are efficacious in intracranial triple-negative breast cancer. Neuro-Oncology. 19(11). 1481–1493. 32 indexed citations

11.

Lee, Benjamin, Laura E. Herring, Brian T. Golitz, et al.. (2017). Application of High Throughput Kinomics Analysis to Identify Inhibitors of Gemcitabine Resistant Pancreatic Cancer Growth. The FASEB Journal. 31(S1). 1 indexed citations

12.

Swearingen, Amanda E.D. Van, Marni B. Siegel, Maria J. Sambade, et al.. (2015). Abstract 2579: Combination therapy with MEK inhibition is efficacious in intracranial triple negative breast cancer models. Cancer Research. 75(15_Supplement). 2579–2579.

13.

Swearingen, Amanda E.D. Van, Marni B. Siegel, Ryan Bash, et al.. (2014). Abstract 5449A: PI3K and MEK inhibition in intracranial triple negative breast cancer: Efficacy of BKM120 and AZD6244 in preclinical mouse models. Cancer Research. 74(19_Supplement). 5449A–5449A.

14.

Gama, Vivian, Vijay Swahari, Johanna M. Schafer, et al.. (2014). The E3 ligase PARC mediates the degradation of cytosolic cytochrome c to promote survival in neurons and cancer cells. Science Signaling. 7(334). ra67–ra67. 55 indexed citations

15.

Zawistowski, Jon S., Kazuhiro Nakamura, Joel S. Parker, et al.. (2013). MicroRNA 9-3p Targets β₁ Integrin To Sensitize Claudin-Low Breast Cancer Cells to MEK Inhibition. Molecular and Cellular Biology. 33(11). 2260–2274. 48 indexed citations

16.

Jordan, Nicole Vincent, Aleix Prat, Amy N. Abell, et al.. (2013). SWI/SNF Chromatin-Remodeling Factor Smarcd3/Baf60c Controls Epithelial-Mesenchymal Transition by Inducing Wnt5a Signaling. Molecular and Cellular Biology. 33(15). 3011–3025. 52 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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