Juan Wang

1.6k citations
107 papers · 1.0k indexed · h-index 18
Topics
Gene expression and cancer classification (34 papers)Bioinformatics and Genomic Networks (32 papers)Machine Learning in Bioinformatics (17 papers)

In The Last Decade

Juan Wang

96 papers receiving 998 citations

Peers

Juan Wang
Comparison fields: 5 of 132
  • Molecular Biology 540
  • Cancer Research 156
  • Infectious Diseases 109
  • Artificial Intelligence 91
  • Genetics 85
Replace Kyungsook Han with:
Kyungsook Han South Korea
T. M. Murali United States
Alexander Schliep Germany
Laurent Jacob France
Cesar Arze United States
Yijie Ding China
Xiaogen Zhou China
Pieter Meysman Belgium
Bo Liao China
Joseph Mellor United States
Juan Wang relative to Kyungsook Han South Korea Kyungsook Han's profile →
Citations per field
00.5×3.4×
Kyungsook Han · 1×
Citations per year

Countries citing papers authored by Juan Wang

Since Specialization
Citations

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

Fields of papers citing papers by Juan Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Juan Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Juan Wang. A scholar is included among the top collaborators of Juan Wang 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 Juan Wang. Juan Wang 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
#WorkIndexed citations
1 2
2 3
3 0
4 10
5 17
6 1
7 1
8 5
9 10
10 1
11 1
12 9
13 18
14 5
15 8
16 4
17 52
18
A graph-Laplacian PCA based on L1/2-norm constraint for characteristic gene selection
3
19
Identifying Metabolite and Protein Biomarkers in Unstable Angina In-patients by Feature Selection Based Data Mining Method
10
20 6

About Juan Wang

Juan Wang is a scholar working on Computational Mathematics, Molecular Biology and Cancer Research, having authored 107 papers that have together received 1.0k indexed citations. Recurring topics across this work include Gene expression and cancer classification (34 papers), Bioinformatics and Genomic Networks (32 papers) and Machine Learning in Bioinformatics (17 papers). The work is most often cited by research in Computational Mathematics (20 citations), Cancer Research (156 citations) and Molecular Biology (540 citations). Juan Wang has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Jin‐Xing Liu, Ying-Lian Gao, Chun-Hou Zheng, Junliang Shang, Ling-Yun Dai, Shasha Yuan, Feifei Tan, Xiangdong Li, Zhen Cui and Xiang-Zhen Kong. Their work appears in journals such as Journal of Biological Chemistry, Scientific Reports and Food 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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