Silva Krause

961 total citations
19 papers, 790 citations indexed

About

Silva Krause is a scholar working on Oncology, Molecular Biology and Cell Biology. According to data from OpenAlex, Silva Krause has authored 19 papers receiving a total of 790 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Oncology, 7 papers in Molecular Biology and 5 papers in Cell Biology. Recurrent topics in Silva Krause's work include Cancer Cells and Metastasis (9 papers), 3D Printing in Biomedical Research (5 papers) and Pancreatic and Hepatic Oncology Research (4 papers). Silva Krause is often cited by papers focused on Cancer Cells and Metastasis (9 papers), 3D Printing in Biomedical Research (5 papers) and Pancreatic and Hepatic Oncology Research (4 papers). Silva Krause collaborates with scholars based in United States, Switzerland and Netherlands. Silva Krause's co-authors include Donald E. Ingber, Ana M. Soto, Maricel V. Maffini, Carlos Sonnenschein, Akiko Mammoto, Joo H. Kang, Mathumai Kanapathipillai, Amy Brock, James J. Collins and A. Bischof and has published in prestigious journals such as Journal of Clinical Oncology, Nature reviews. Cancer and PLoS ONE.

In The Last Decade

Silva Krause

18 papers receiving 778 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Silva Krause United States 13 372 358 262 160 145 19 790
Seishi Kono Japan 9 388 1.0× 397 1.1× 384 1.5× 103 0.6× 168 1.2× 15 940
John-Patrick Mpindi Finland 12 237 0.6× 277 0.8× 380 1.5× 141 0.9× 172 1.2× 16 823
Sofia P. Rebelo Portugal 9 356 1.0× 296 0.8× 288 1.1× 311 1.9× 61 0.4× 13 856
Jessica Hoarau-Véchot Qatar 11 264 0.7× 219 0.6× 229 0.9× 73 0.5× 78 0.5× 13 636
Alina Starchenko United States 14 299 0.8× 306 0.9× 335 1.3× 271 1.7× 98 0.7× 20 881
Gowri Manohari Balachander India 8 380 1.0× 281 0.8× 231 0.9× 70 0.4× 62 0.4× 18 776
Ying Bena Lim Singapore 11 259 0.7× 221 0.6× 308 1.2× 309 1.9× 138 1.0× 14 830
Agata Nyga United Kingdom 12 384 1.0× 276 0.8× 152 0.6× 156 1.0× 71 0.5× 18 690
Myrofora Panagi Cyprus 15 297 0.8× 312 0.9× 297 1.1× 84 0.5× 136 0.9× 24 883

Countries citing papers authored by Silva Krause

Since Specialization
Citations

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

Fields of papers citing papers by Silva Krause

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Silva Krause

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

All Works

19 of 19 papers shown
1.
Knebelmann, Bertrand, Kate Bramham, Kieran McCafferty, et al.. (2023). #2528 APOL1 GENOTYPING AND PROTEINURIC KIDNEY DISEASE IN EUROPE. Nephrology Dialysis Transplantation. 38(Supplement_1).
2.
O’Reilly, Eileen M., Devalingam Mahalingam, Tanios Bekaii‐Saab, et al.. (2020). Randomised phase II trial of gemcitabine and nab-paclitaxel with necuparanib or placebo in untreated metastatic pancreas ductal adenocarcinoma. European Journal of Cancer. 132. 112–121. 27 indexed citations
3.
Guess, Jamey, Chia Lin Chu, Edward Cochran, et al.. (2018). Necuparanib, A Multitargeting Heparan Sulfate Mimetic, Targets Tumor and Stromal Compartments in Pancreatic Cancer. Molecular Cancer Therapeutics. 18(2). 245–256. 18 indexed citations
4.
5.
O’Reilly, Eileen M., Devalingam Mahalingam, James Roach, et al.. (2017). Necuparanib combined with nab-paclitaxel + gemcitabine in patients with metastatic pancreatic cancer: Phase 2 results.. Journal of Clinical Oncology. 35(4_suppl). 370–370. 5 indexed citations
6.
O’Reilly, Eileen M., Devalingam Mahalingam, James Roach, et al.. (2016). Safety, pharmacokinetics, pharmacodynamics, and antitumor activity of necuparanib combined with nab-paclitaxel and gemcitabine in patients with metastatic pancreatic cancer: Updated phase 1 results.. Journal of Clinical Oncology. 34(15_suppl). 4117–4117. 2 indexed citations
7.
Brock, Amy, Silva Krause, & Donald E. Ingber. (2015). Control of cancer formation by intrinsic genetic noise and microenvironmental cues. Nature reviews. Cancer. 15(8). 499–509. 44 indexed citations
8.
Krause, Silva, et al.. (2015). Abstract 5499: Necuparanib affects tumor progression and invasion in a 3D co-culture system of pancreatic cancer cells and stellate cells. Cancer Research. 75(15_Supplement). 5499–5499. 3 indexed citations
9.
Brock, Amy, Silva Krause, Hu Li, et al.. (2014). Silencing HoxA1 by Intraductal Injection of siRNA Lipidoid Nanoparticles Prevents Mammary Tumor Progression in Mice. Science Translational Medicine. 6(217). 217ra2–217ra2. 64 indexed citations
10.
Werfel, Justin, Silva Krause, A. Bischof, et al.. (2013). How Changes in Extracellular Matrix Mechanics and Gene Expression Variability Might Combine to Drive Cancer Progression. PLoS ONE. 8(10). e76122–e76122. 24 indexed citations
11.
Krause, Silva, Amy Brock, & Donald E. Ingber. (2013). Intraductal Injection for Localized Drug Delivery to the Mouse Mammary Gland. Journal of Visualized Experiments. 30 indexed citations
12.
Bischof, A., Deniz Yüksel, Tadanori Mammoto, et al.. (2013). Breast cancer normalization induced by embryonic mesenchyme is mediated by extracellular matrix biglycan. Integrative Biology. 5(8). 1045–1056. 34 indexed citations
13.
Krause, Silva, Amy Brock, Michael S. Goldberg, & Donald E. Ingber. (2013). Abstract 2025: Prevention of mammary tumor progression via intraductal injection of nanoparticle-formulated siRNA.. Cancer Research. 73(8_Supplement). 2025–2025. 1 indexed citations
14.
Krause, Silva, Amy Brock, & Donald E. Ingber. (2013). Intraductal Injection for Localized Drug Delivery to the Mouse Mammary Gland. Journal of Visualized Experiments. 12 indexed citations
15.
Brock, Amy, et al.. (2012). Abstract 4920: Gene network models identify targets for mammary tumor normalization. Cancer Research. 72(8_Supplement). 4920–4920. 3 indexed citations
16.
Kang, Joo H., et al.. (2012). A combined micromagnetic-microfluidic device for rapid capture and culture of rare circulating tumor cells. Lab on a Chip. 12(12). 2175–2175. 240 indexed citations
17.
Krause, Silva, et al.. (2011). Dual Regulation of Breast Tubulogenesis Using Extracellular Matrix Composition and Stromal Cells. Tissue Engineering Part A. 18(5-6). 520–532. 19 indexed citations
18.
Krause, Silva, Maricel V. Maffini, Ana M. Soto, & Carlos Sonnenschein. (2010). The microenvironment determines the breast cancer cells' phenotype: organization of MCF7 cells in 3D cultures. BMC Cancer. 10(1). 263–263. 107 indexed citations
19.
Krause, Silva, Maricel V. Maffini, Ana M. Soto, & Carlos Sonnenschein. (2008). A Novel 3D In Vitro Culture Model to Study Stromal–Epithelial Interactions in the Mammary Gland. Tissue Engineering Part C Methods. 14(3). 261–271. 121 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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