Peter Pan

415 citations
13 papers · 282 · h-index 7

Impact in

  • Genetics top 10%
    • Glioma Diagnosis and Treatment
    • Neural and Behavioral Psychology Studies
    • Neural dynamics and brain function
    • EEG and Brain-Computer Interfaces
    • Neuroscience and Music Perception
    • Visual perception and processing mechanisms

Papers in

Peter Pan

12 papers receiving 278 citations

Peers

Peter Pan
Comparison fields: 5 of 68
  • Genetics 80
  • Cognitive Neuroscience 93
  • Oncology 51
  • Cancer Research 23
  • Neurology 21
Replace Jun‐ichi Nagai with:
Jun‐ichi Nagai Japan
Chunhong Qin China
Antonios Vakis Greece
Edward J. Estlin United Kingdom
Afua A. Akuffo United States
Katrina Moore United Kingdom
Lan Pham United States
Sharona Yashar United States
Barbara Link Germany
Peter Pan relative to Jun‐ichi Nagai Japan Jun‐ichi Nagai's profile →
Citations per field
00.5×3.6×
Jun‐ichi Nagai · 1×
Citations per year

Countries citing papers authored by Peter Pan

Since Specialization
Citations

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

Fields of papers citing papers by Peter Pan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Peter Pan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Peter Pan Line = papers co-authored together Peter Pan links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1 201198
2 202059
3 201947
4 202037
5 199713
6 202010
7 20237
8 20234
9 20214
10 20241
11 20201
12 20251
13 20230

About Peter Pan

Peter Pan is a scholar working on Genetics, Pulmonary and Respiratory Medicine, Cognitive Neuroscience, Oncology and Epidemiology, having authored 13 papers that have together received 282 indexed citations. Recurring topics across this work include Glioma Diagnosis and Treatment (6 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Brain Metastases and Treatment (2 papers), Neural and Behavioral Psychology Studies (2 papers), Meningioma and schwannoma management (2 papers), Cognitive Functions and Memory (1 paper), Inflammatory mediators and NSAID effects (1 paper) and Neural dynamics and brain function (1 paper). The work is most often cited by research in Genetics (80 citations), Cognitive Neuroscience (93 citations), Oncology (51 citations), Cancer Research (23 citations) and Neurology (21 citations). Peter Pan has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Rajiv Magge, Aya Haggiagi, Theodore P. Zanto, Adam Gazzaley, Anna C. Nobre, Jacob Bollinger, Andrew B. Lassman, Wolfgang Wick, Zwe‐Ling Kong and Francis G. Fang. Their work appears in journals such as Neuro-Oncology, Neuro-Oncology Advances, Frontiers in Oncology, Journal of Neuroscience and Cortex.

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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