Ming‐Fei Lang

1.4k citations
31 papers · 1.1k · h-index 12

Impact in

Papers in

Ming‐Fei Lang

28 papers receiving 1.1k citations

Peers

Ming‐Fei Lang
Comparison fields: 5 of 100
  • Developmental Neuroscience 169
  • Cancer Research 474
  • Molecular Biology 633
  • Neurology 58
  • Electrochemistry 36
Replace Lingyu Zhao with:
Lingyu Zhao China
Tamara Roitbak United States
Benayahu Elbaz United States
Divya M. Chari United Kingdom
Alfred Xuyang Sun Singapore
Miriam S. Domowicz United States
Francesca Gullo Italy
Jianjun Ma China
Dong-Kyu Park South Korea
Jung Min Lee South Korea
Ming‐Fei Lang relative to Lingyu Zhao China Lingyu Zhao's profile →
Citations per field
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Lingyu Zhao · 1×
Citations per year

Countries citing papers authored by Ming‐Fei Lang

Since Specialization
Citations

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

Fields of papers citing papers by Ming‐Fei Lang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ming‐Fei Lang. 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 Ming‐Fei Lang. The network helps show where Ming‐Fei Lang may publish in the future.

Co-authors

The 25 scholars most cited alongside Ming‐Fei Lang, 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 Ming‐Fei Lang Line = papers co-authored together Ming‐Fei Lang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2010327
2 2011247
3 201295
4 201190
5 201272
6 201843
7 202138
8 199334
9 200227
10 202022
11 201121
12 201916
13 201311
14 201710
15 201910
16 20218
17 20077
18 20246
19 20146
20 20205

About Ming‐Fei Lang

Ming‐Fei Lang is a scholar working on Molecular Biology, Electrical and Electronic Engineering, Cancer Research, Electrochemistry and Biomedical Engineering, having authored 31 papers that have together received 1.1k indexed citations. Recurring topics across this work include Circular RNAs in diseases (6 papers), MicroRNA in disease regulation (6 papers), Electrochemical Analysis and Applications (6 papers), Neurogenesis and neuroplasticity mechanisms (4 papers), Electrocatalysts for Energy Conversion (4 papers), Advanced Sensor and Energy Harvesting Materials (3 papers), Single-cell and spatial transcriptomics (3 papers) and Electrochemical sensors and biosensors (3 papers). The work is most often cited by research in Developmental Neuroscience (169 citations), Cancer Research (474 citations), Molecular Biology (633 citations), Neurology (58 citations) and Electrochemistry (36 citations). Ming‐Fei Lang has collaborated with scholars based in China, United States and Germany. Frequent co-authors include Yanhong Shi, Guoqiang Sun, Wendong Li, LI Sheng-xiu, Yang Su, Chunnian Zhao, Kiyohito Murai, Jing Sun, Jason Z. Yin and Allen Wang. Their work appears in journals such as International Journal of Hydrogen Energy, Nature Communications, Pediatric Research, Journal of Materials Research and Technology and Analytical Chemistry.

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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