John-Patrick Mpindi

2.3k total citations
16 papers, 823 citations indexed

About

John-Patrick Mpindi is a scholar working on Molecular Biology, Oncology and Cancer Research. According to data from OpenAlex, John-Patrick Mpindi has authored 16 papers receiving a total of 823 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 5 papers in Oncology and 5 papers in Cancer Research. Recurrent topics in John-Patrick Mpindi's work include Computational Drug Discovery Methods (2 papers), Molecular Biology Techniques and Applications (2 papers) and MicroRNA in disease regulation (2 papers). John-Patrick Mpindi is often cited by papers focused on Computational Drug Discovery Methods (2 papers), Molecular Biology Techniques and Applications (2 papers) and MicroRNA in disease regulation (2 papers). John-Patrick Mpindi collaborates with scholars based in Finland, Sweden and Netherlands. John-Patrick Mpindi's co-authors include Olli Kallioniemi, Pekka Kohonen, Rami Mäkelä, Matthias Nees, Merja Perälä, Jyrki Lötjönen, Matias Knuuttila, Johannes Virtanen, Ville Härmä and Antti P. Happonen and has published in prestigious journals such as Bioinformatics, PLoS ONE and Cancer Research.

In The Last Decade

John-Patrick Mpindi

16 papers receiving 811 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John-Patrick Mpindi Finland 12 380 277 237 172 141 16 823
Johannes Virtanen Finland 8 308 0.8× 210 0.8× 179 0.8× 100 0.6× 106 0.8× 9 655
Ville Härmä Finland 10 289 0.8× 232 0.8× 211 0.9× 70 0.4× 122 0.9× 16 646
Jeremy R. Semeiks United States 5 378 1.0× 548 2.0× 365 1.5× 177 1.0× 264 1.9× 7 1.0k
Valérie Rouffiac France 16 302 0.8× 190 0.7× 189 0.8× 182 1.1× 42 0.3× 31 694
Marc Osterland Germany 8 316 0.8× 156 0.6× 163 0.7× 114 0.7× 91 0.6× 10 617
Mercedes Lioni United States 11 833 2.2× 697 2.5× 240 1.0× 180 1.0× 209 1.5× 11 1.4k
Silva Krause United States 13 262 0.7× 358 1.3× 372 1.6× 145 0.8× 160 1.1× 19 790
Johann Kern Germany 18 454 1.2× 208 0.8× 109 0.5× 162 0.9× 128 0.9× 55 863
Kyle A. DiVito United States 16 508 1.3× 273 1.0× 95 0.4× 119 0.7× 59 0.4× 24 767
Virginie Dangles-Marie France 8 459 1.2× 787 2.8× 509 2.1× 258 1.5× 185 1.3× 8 1.3k

Countries citing papers authored by John-Patrick Mpindi

Since Specialization
Citations

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

Fields of papers citing papers by John-Patrick Mpindi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John-Patrick Mpindi. 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 John-Patrick Mpindi. The network helps show where John-Patrick Mpindi may publish in the future.

Co-authorship network of co-authors of John-Patrick Mpindi

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

All Works

16 of 16 papers shown
1.
Fagerholm, Rainer, Taru Muranen, Sippy Kaur, et al.. (2021). High miR-30 Expression Associates with Improved Breast Cancer Patient Survival and Treatment Outcome. Cancers. 13(12). 2907–2907. 4 indexed citations
2.
Potdar, Swapnil, Aleksandr Ianevski, John-Patrick Mpindi, et al.. (2020). Breeze: an integrated quality control and data analysis application for high-throughput drug screening. Bioinformatics. 36(11). 3602–3604. 63 indexed citations
3.
Pellinen, Teijo, Sami Blom, Katja Välimäki, et al.. (2018). ITGB1-dependent upregulation of Caveolin-1 switches TGFβ signalling from tumour-suppressive to oncogenic in prostate cancer. Scientific Reports. 8(1). 2338–2338. 27 indexed citations
4.
Kangaspeska, Sara, Alok Jaiswal, Henrik Edgren, et al.. (2016). Systematic drug screening reveals specific vulnerabilities and co-resistance patterns in endocrine-resistant breast cancer. BMC Cancer. 16(1). 378–378. 10 indexed citations
5.
Lehtinen, Laura, Katja Kaipio, John-Patrick Mpindi, et al.. (2016). REG4 Is Highly Expressed in Mucinous Ovarian Cancer: A Potential Novel Serum Biomarker. PLoS ONE. 11(3). e0151590–e0151590. 17 indexed citations
6.
Pellinen, Teijo, Sami Blom, Katja Välimäki, et al.. (2015). Abstract 207: Caveolin-1 drives oncogenic TGFβ effects in prostate cancer: in vitro mechanistic insights integrated with systems pathology visualization in primary tumor samples. Cancer Research. 75(15_Supplement). 207–207. 1 indexed citations
7.
Pemovska, Tea, Mika Kontro, Bhagwan Yadav, et al.. (2015). Novel drug candidates for blast phase chronic myeloid leukemia from high-throughput drug sensitivity and resistance testing. Blood Cancer Journal. 5(5). e309–e309. 18 indexed citations
8.
Aakula, Anna, Pekka Kohonen, Suvi‐Katri Leivonen, et al.. (2015). Systematic Identification of MicroRNAs That Impact on Proliferation of Prostate Cancer Cells and Display Changed Expression in Tumor Tissue. European Urology. 69(6). 1120–1128. 49 indexed citations
9.
Mpindi, John-Patrick, Swapnil Potdar, Dmitrii Bychkov, et al.. (2015). Impact of normalization methods on high-throughput screening data with high hit rates and drug testing with dose–response data. Bioinformatics. 31(23). 3815–3821. 27 indexed citations
10.
Karinen, Sirkku, Kaisa Auvinen, Heikki Irjala, et al.. (2013). Correction: Plasticity of Blood- and Lymphatic Endothelial Cells and Marker Identification. PLoS ONE. 8(10). 7 indexed citations
11.
Karinen, Sirkku, Kaisa Auvinen, Heikki Irjala, et al.. (2013). Plasticity of Blood- and Lymphatic Endothelial Cells and Marker Identification. PLoS ONE. 8(9). e74293–e74293. 22 indexed citations
12.
Hongisto, Vesa, Sandra Jernström, Vidal Fey, et al.. (2013). High-Throughput 3D Screening Reveals Differences in Drug Sensitivities between Culture Models of JIMT1 Breast Cancer Cells. PLoS ONE. 8(10). e77232–e77232. 137 indexed citations
13.
Pellinen, Teijo, Juha Rantala, Antti Arjonen, et al.. (2012). A functional genetic screen reveals new regulators of β1-integrin activity. Journal of Cell Science. 125(3). 649–661. 33 indexed citations
14.
Vainio, Paula, Santosh Gupta, Kirsi Ketola, et al.. (2011). Arachidonic Acid Pathway Members PLA2G7, HPGD, EPHX2, and CYP4F8 Identified as Putative Novel Therapeutic Targets in Prostate Cancer. American Journal Of Pathology. 178(2). 525–536. 92 indexed citations
15.
Rantala, Juha, Rami Mäkelä, John-Patrick Mpindi, et al.. (2011). A cell spot microarray method for production of high density siRNA transfection microarrays. BMC Genomics. 12(1). 162–162. 40 indexed citations
16.
Härmä, Ville, Johannes Virtanen, Rami Mäkelä, et al.. (2010). A Comprehensive Panel of Three-Dimensional Models for Studies of Prostate Cancer Growth, Invasion and Drug Responses. PLoS ONE. 5(5). e10431–e10431. 276 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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