Mono Pirun

5.8k total citations · 2 hit papers
7 papers, 1.3k citations indexed

About

Mono Pirun is a scholar working on Molecular Biology, Cancer Research and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Mono Pirun has authored 7 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Molecular Biology, 4 papers in Cancer Research and 2 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Mono Pirun's work include Cancer Genomics and Diagnostics (3 papers), RNA modifications and cancer (1 paper) and Tuberous Sclerosis Complex Research (1 paper). Mono Pirun is often cited by papers focused on Cancer Genomics and Diagnostics (3 papers), RNA modifications and cancer (1 paper) and Tuberous Sclerosis Complex Research (1 paper). Mono Pirun collaborates with scholars based in United States. Mono Pirun's co-authors include Nicholas D. Socci, Christopher E. Mason, Azra Krek, Raya Khanin, Yupu Liang, Paul Zumbo, Franck Rapaport, Doron Betel, Agnès Viale and David B. Solit and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Blood.

In The Last Decade

Mono Pirun

7 papers receiving 1.2k 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.

Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data

2013 491 citations Franck Rapaport, Raya Khanin et al. Genome biology profile →
Genome Sequencing Identifies a Basis for Everolimus Sensitivity

2012 480 citations Gopa Iyer, Aphrothiti J. Hanrahan et al. Science profile →

Peers

Mono Pirun

MB

CR

ONCOL

PRM

SURGE

Aleksandra Dakic United States

Jingfeng Li China

Jungho Kim South Korea

Masahide Seki Japan

Maddalena Arigoni Italy

Ayako Suzuki Japan

Job van Riet Netherlands

Christer Larsson Sweden

Sander Boymans Netherlands

Aleksandra Dakic United States

Mono Pirun

555 ×0.8

MB

186 ×0.5

CR

369 ×1.4

ONCOL

265 ×1.2

PRM

133 ×0.7

SURGE

Citations per year, relative to Mono Pirun Mono Pirun (= 1×) peers Aleksandra Dakic

Countries citing papers authored by Mono Pirun

Since Specialization

Citations

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

Fields of papers citing papers by Mono Pirun

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mono Pirun

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

All Works

Sort: Min cites: Since: Top N: Style:

7 of 7 papers shown

1.

McFadden, David G., Katerina Politi, Arjun Bhutkar, et al.. (2016). Mutational landscape of EGFR- , MYC- , and Kras- driven genetically engineered mouse models of lung adenocarcinoma. Proceedings of the National Academy of Sciences. 113(42). E6409–E6417. 126 indexed citations

2.

Jacob, Leni S., Sakari Vanharanta, Anna C. Obenauf, et al.. (2015). Metastatic Competence Can Emerge with Selection of Preexisting Oncogenic Alleles without a Need of New Mutations. Cancer Research. 75(18). 3713–3719. 42 indexed citations

3.

Rapaport, Franck, Raya Khanin, Yupu Liang, et al.. (2013). Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome biology. 14(9). R95–R95. 491 indexed citations breakdown →

4.

Schatz, Jonathan H., Steven M. Horwitz, Matthew A. Lunning, et al.. (2013). Next-Generation Sequencing Suggests Complex, Heterogeneous Pathogenesis In Peripheral T-Cell Lymphoma Unspecified. Blood. 122(21). 843–843. 2 indexed citations

5.

Iyer, Gopa, Aphrothiti J. Hanrahan, Matthew I. Milowsky, et al.. (2012). Genome Sequencing Identifies a Basis for Everolimus Sensitivity. Science. 338(6104). 221–221. 480 indexed citations breakdown →

6.

Pirun, Mono, G. Babnigg, & Fred J. Stevens. (2004). Template-based recognition of protein fold within the midnight and twilight zones of protein sequence similarity. Journal of Molecular Recognition. 18(3). 203–212. 3 indexed citations

7.

Mauney, Joshua R., et al.. (2004). Osteogenic Differentiation of Human Bone Marrow Stromal Cells on Partially Demineralized Bone Scaffolds in Vitro. Tissue Engineering. 10(1-2). 81–92. 107 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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