Ying-Lian Gao

1.4k citations
89 papers · 1.0k indexed · h-index 19

Ying-Lian Gao

85 papers receiving 993 citations

Peers

Ying-Lian Gao
Comparison fields: 5 of 94
  • Computational Mathematics 33
  • Cancer Research 272
  • Computer Vision and Pattern Recognition 204
  • Molecular Biology 657
  • Computational Theory and Mathematics 115
Replace Xiang-Zhen Kong with:
Xiang-Zhen Kong China
Xiao Zheng China
De-Shuang Huang China
Yingke Lei China
Yaohua Hu China
Dijun Luo United States
Georgios Goumas Greece
Jeongyoun Ahn United States
Hong Peng China
Chun Tang United States
Ying-Lian Gao relative to Xiang-Zhen Kong China Xiang-Zhen Kong's profile →
Citations per field
00.5×4.1×
Xiang-Zhen Kong · 1×
Citations per year

Countries citing papers authored by Ying-Lian Gao

Since Specialization
Citations

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

Fields of papers citing papers by Ying-Lian Gao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ying-Lian Gao. 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 Ying-Lian Gao. The network helps show where Ying-Lian Gao may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Ying-Lian Gao, 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 Ying-Lian Gao Line = papers co-authored together Ying-Lian Gao links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 202414
3 20240
4 20242
5 20237
6 20239
7 20231
8 20232
9 20238
10 20235
11 202310
12 202218
13 202218
14 20227
15 202036
16 201919
17 201917
18 201754
19
A graph-Laplacian PCA based on L1/2-norm constraint for characteristic gene selection
20163
20 20140

About Ying-Lian Gao

Ying-Lian Gao is a scholar working on Computational Mathematics, Cancer Research and Molecular Biology, having authored 89 papers that have together received 1.0k indexed citations. Recurring topics across this work include Gene expression and cancer classification (42 papers), Bioinformatics and Genomic Networks (32 papers), Machine Learning in Bioinformatics (23 papers), Cancer-related molecular mechanisms research (17 papers), MicroRNA in disease regulation (15 papers), Computational Drug Discovery Methods (10 papers), Face and Expression Recognition (10 papers) and Circular RNAs in diseases (9 papers). The work is most often cited by research in Computational Mathematics (33 citations), Cancer Research (272 citations) and Computer Vision and Pattern Recognition (204 citations). Ying-Lian Gao has collaborated with scholars based in China and Hong Kong. Frequent co-authors include Jin‐Xing Liu, Chun-Hou Zheng, Junliang Shang, Zhen Cui, Juan Wang, Xiang-Zhen Kong, Yong Xu, Chun-Mei Feng, Jiguo Yu and Na Yu. Their work appears in journals such as IEEE Journal of Biomedical and Health Informatics, IEEE/ACM Transactions on Computational Biology and Bioinformatics, BMC Bioinformatics, IEEE Transactions on NanoBioscience and PLoS ONE.

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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