Chin Lung Lu

1.1k total citations
43 papers, 608 citations indexed

About

Chin Lung Lu is a scholar working on Molecular Biology, Artificial Intelligence and Genetics. According to data from OpenAlex, Chin Lung Lu has authored 43 papers receiving a total of 608 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Molecular Biology, 13 papers in Artificial Intelligence and 13 papers in Genetics. Recurrent topics in Chin Lung Lu's work include Genomics and Phylogenetic Studies (23 papers), RNA and protein synthesis mechanisms (18 papers) and Genome Rearrangement Algorithms (12 papers). Chin Lung Lu is often cited by papers focused on Genomics and Phylogenetic Studies (23 papers), RNA and protein synthesis mechanisms (18 papers) and Genome Rearrangement Algorithms (12 papers). Chin Lung Lu collaborates with scholars based in Taiwan and India. Chin Lung Lu's co-authors include Chuan Yi Tang, Yen‐Lin Huang, Hsien‐Tai Chiu, Ming‐Tat Ko, Chih‐Wei Wang, Ying Lin, Hwan‐You Chang, Shih‐Yuan Huang, Ying-Tsong Chen and Hwei‐Ling Peng and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and BMC Bioinformatics.

In The Last Decade

Chin Lung Lu

41 papers receiving 587 citations

Peers

Chin Lung Lu
Comparison fields: 5 of 80
  • Molecular Biology 350
  • Computational Theory and Mathematics 157
  • Genetics 137
  • Artificial Intelligence 117
  • Plant Science 106
Replace Ekaterina M. Panina with:
Ekaterina M. Panina Russia
Carsten Damm Germany
Gilles Bernot France
Thomas Conway Australia
Phillip E. C. Compeau United States
Roman Kolpakov Russia
Uri Keich United States
Philip J. Hatcher United States
Dustin Cartwright United States
Krzysztof Giaro Poland
Ekaterina M. Panina Russia View profile →
Citations per field, relative to Chin Lung Lu
Chin Lung Lu · 1×
Citations per year, relative to Chin Lung Lu
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Countries citing papers authored by Chin Lung Lu

Since Specialization
Citations

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

Fields of papers citing papers by Chin Lung Lu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chin Lung Lu. 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 Chin Lung Lu. The network helps show where Chin Lung Lu may publish in the future.

Co-authorship network of co-authors of Chin Lung Lu

This figure shows the co-authorship network connecting the top 25 collaborators of Chin Lung Lu. A scholar is included among the top collaborators of Chin Lung Lu 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 Chin Lung Lu. Chin Lung Lu 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
# Work Indexed citations
1 1
2 10
3 1
4 7
5 1
6 3
7 36
8 7
9 29
10 34
11 0
12 16
13 2
14 24
15 29
16 31
17 4
18 31
19 23
20 30

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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