Dan Peer

1.0k total citations
24 papers, 776 citations indexed

About

Dan Peer is a scholar working on Molecular Biology, Oncology and Immunology. According to data from OpenAlex, Dan Peer has authored 24 papers receiving a total of 776 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 6 papers in Oncology and 5 papers in Immunology. Recurrent topics in Dan Peer's work include RNA Interference and Gene Delivery (10 papers), Advanced biosensing and bioanalysis techniques (5 papers) and Extracellular vesicles in disease (4 papers). Dan Peer is often cited by papers focused on RNA Interference and Gene Delivery (10 papers), Advanced biosensing and bioanalysis techniques (5 papers) and Extracellular vesicles in disease (4 papers). Dan Peer collaborates with scholars based in Israel, United States and United Kingdom. Dan Peer's co-authors include Riccardo Rampado, Daniel Rosenblum, Inbal Hazan‐Halevy, Shiri Weinstein, Osnat Bairey, Pia Raanani, Srinivas Ramishetti, Niels Dammes, Meir Goldsmith and Edo Kon and has published in prestigious journals such as Advanced Materials, ACS Nano and Circulation Research.

In The Last Decade

Dan Peer

24 papers receiving 766 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dan Peer Israel 12 540 163 151 97 93 24 776
Jilian R. Melamed United States 14 638 1.2× 83 0.5× 175 1.2× 155 1.6× 121 1.3× 22 884
Christian Boada United States 13 647 1.2× 127 0.8× 260 1.7× 204 2.1× 183 2.0× 16 1.0k
Xin Zeng China 15 720 1.3× 173 1.1× 102 0.7× 134 1.4× 102 1.1× 30 966
Shan Guan China 19 796 1.5× 113 0.7× 134 0.9× 137 1.4× 156 1.7× 51 1.2k
Hassan Dana Iran 9 571 1.1× 211 1.3× 87 0.6× 63 0.6× 100 1.1× 18 797
Sushmita Chatterjee Israel 13 623 1.2× 75 0.5× 114 0.8× 131 1.4× 153 1.6× 18 847
Shan Zong China 14 295 0.5× 163 1.0× 164 1.1× 195 2.0× 67 0.7× 29 718
Pierre‐Alain Burnouf Taiwan 12 456 0.8× 94 0.6× 173 1.1× 221 2.3× 85 0.9× 15 738
Jiayi Qian China 15 530 1.0× 180 1.1× 114 0.8× 109 1.1× 63 0.7× 55 864
Marshall S. Padilla United States 16 616 1.1× 50 0.3× 152 1.0× 151 1.6× 145 1.6× 27 931

Countries citing papers authored by Dan Peer

Since Specialization
Citations

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

Fields of papers citing papers by Dan Peer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dan Peer

This figure shows the co-authorship network connecting the top 25 collaborators of Dan Peer. A scholar is included among the top collaborators of Dan Peer 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 Dan Peer. Dan Peer 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.
Zhang, Meng, Huifang Nie, Guangwei Jiang, et al.. (2025). Photo-driven bacterial motor for tumor-targeted deep penetration and destruction while reducing hepatotoxicity. Nano Today. 63. 102752–102752. 1 indexed citations
2.
Ad‐El, Nitay, Meir Goldsmith, Anna Gutkin, et al.. (2025). Low-frequency ultrasound-mediated blood-brain barrier opening enables non-invasive lipid nanoparticle RNA delivery to glioblastoma. Journal of Controlled Release. 385. 114018–114018. 4 indexed citations
3.
Rampado, Riccardo & Dan Peer. (2023). Design of experiments in the optimization of nanoparticle-based drug delivery systems. Journal of Controlled Release. 358. 398–419. 72 indexed citations
4.
Hazan‐Halevy, Inbal, Meir Goldsmith, Lior Stotsky‐Oterin, et al.. (2023). Delivery of Therapeutic RNA to the Bone Marrow in Multiple Myeloma Using CD38‐Targeted Lipid Nanoparticles (Adv. Sci. 21/2023). Advanced Science. 10(21). 2 indexed citations
5.
Ezra, Assaf, et al.. (2023). Lipid nanoparticles-loaded with toxin mRNA represents a new strategy for the treatment of solid tumors. Theranostics. 13(11). 3497–3508. 12 indexed citations
6.
Aday, Sezin, Inbal Hazan‐Halevy, Aránzazu Chamorro-Jorganes, et al.. (2021). Bioinspired artificial exosomes based on lipid nanoparticles carrying let-7b-5p promote angiogenesis in vitro and in vivo. Molecular Therapy. 29(7). 2239–2252. 57 indexed citations
7.
Singh, Manu Smriti, Srinivas Ramishetti, Dalit Landesman‐Milo, et al.. (2021). Gene Silencing: Therapeutic Gene Silencing Using Targeted Lipid Nanoparticles in Metastatic Ovarian Cancer (Small 19/2021). Small. 17(19). 1 indexed citations
8.
Kon, Edo, et al.. (2019). Therapeutic mRNA delivery to leukocytes. Journal of Controlled Release. 305. 165–175. 44 indexed citations
9.
Hazan‐Halevy, Inbal, Daniel Rosenblum, Srinivas Ramishetti, & Dan Peer. (2019). Systemic Modulation of Lymphocyte Subsets Using siRNAs Delivered via Targeted Lipid Nanoparticles. Methods in molecular biology. 1974. 151–159. 5 indexed citations
10.
Veiga, Nuphar, Meir Goldsmith, Yael Diesendruck, et al.. (2019). Leukocyte-specific siRNA delivery revealing IRF8 as a potential anti-inflammatory target. Journal of Controlled Release. 313. 33–41. 46 indexed citations
11.
Aday, Sezin, Maryam Anwar, Paolo Madeddu, et al.. (2019). Abstract 130: Development of Bioinspired Synthetic Exosomes With Proangiogenic Potential. Circulation Research. 125(Suppl_1). 1 indexed citations
12.
Ramishetti, Srinivas & Dan Peer. (2018). Engineering lymphocytes with RNAi. Advanced Drug Delivery Reviews. 141. 55–66. 25 indexed citations
13.
Álvarez, Mario Moisés, Joanna Aizenberg, Mostafa Analoui, et al.. (2017). Emerging Trends in Micro- and Nanoscale Technologies in Medicine: From Basic Discoveries to Translation. ACS Nano. 11(6). 5195–5214. 101 indexed citations
14.
Aday, Sezin, Maryam Anwar, Marie Besnier, et al.. (2017). P181Artificial exosomes for post-ischemic vascular regeneration. European Heart Journal. 38(suppl_1). 1 indexed citations
15.
Hazan‐Halevy, Inbal, et al.. (2016). Immunomodulation of hematological malignancies using oligonucleotides based-nanomedicines. Journal of Controlled Release. 244(Pt B). 149–156. 18 indexed citations
16.
Hazan‐Halevy, Inbal, Daniel Rosenblum, Shiri Weinstein, et al.. (2015). Cell-specific uptake of mantle cell lymphoma-derived exosomes by malignant and non-malignant B-lymphocytes. Cancer Letters. 364(1). 59–69. 125 indexed citations
17.
Peer, Dan, et al.. (2014). Modulating cancer multidrug resistance by sertraline in combination with a nanomedicine. Cancer Letters. 354(2). 290–298. 59 indexed citations
18.
Peer, Dan. (2014). Precision medicine – Delivering the goods?. Cancer Letters. 352(1). 2–3. 22 indexed citations
19.
Peer, Dan. (2014). Featuring the special issue guest editor: Dan Peer, Ph.D. Cancer Letters. 352(1). 1–1. 1 indexed citations
20.
Wang, Xiangdong, Dan Peer, & Bryon E. Petersen. (2013). Molecular and Cellular Therapies: New challenges and opportunities. PubMed. 1(1). 1–1. 7 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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