Yu Fan

9.0k total citations · 1 hit paper
39 papers, 1.5k citations indexed

About

Yu Fan is a scholar working on Molecular Biology, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Yu Fan has authored 39 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 9 papers in Artificial Intelligence and 8 papers in Computer Vision and Pattern Recognition. Recurrent topics in Yu Fan's work include Genomics and Phylogenetic Studies (7 papers), Genomics and Rare Diseases (4 papers) and Bayesian Modeling and Causal Inference (4 papers). Yu Fan is often cited by papers focused on Genomics and Phylogenetic Studies (7 papers), Genomics and Rare Diseases (4 papers) and Bayesian Modeling and Causal Inference (4 papers). Yu Fan collaborates with scholars based in China, United States and France. Yu Fan's co-authors include Paul O. Lewis, Lynn Kuo, Wangang Xie, Ming‐Hui Chen, Wenyi Wang, Rui Wu, Ming‐Hui Chen, Christian R. Shelton, Jianjun Zhang and Xi Liu and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Scientific Reports and Water Resources Research.

In The Last Decade

Yu Fan

35 papers receiving 1.5k citations

Hit Papers

Improving Marginal Likelihood Estimation for Bayesian Phy... 2010 2026 2015 2020 2010 250 500 750
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Improving Marginal Likelihood Estimation for Bayesian Phylogenetic Model Selection
    2010 773 citations Wangang Xie, Paul O. Lewis et al. Systematic Biology profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Yu Fan China 15 581 501 311 274 185 39 1.5k
Aakrosh Ratan United States 26 846 1.5× 627 1.3× 238 0.8× 139 0.5× 130 0.7× 68 1.8k
Lynn Kuo United States 22 740 1.3× 483 1.0× 309 1.0× 282 1.0× 47 0.3× 79 2.5k
Lin Xia China 22 546 0.9× 410 0.8× 234 0.8× 283 1.0× 134 0.7× 76 1.6k
Ilan Gronau Israel 15 727 1.3× 1.1k 2.1× 119 0.4× 100 0.4× 79 0.4× 26 1.6k
Vincent Cahais France 17 831 1.4× 750 1.5× 258 0.8× 110 0.4× 122 0.7× 28 1.7k
Dirk Metzler Germany 28 1.5k 2.5× 694 1.4× 352 1.1× 76 0.3× 410 2.2× 82 3.1k
Leonidas Salichos United States 13 991 1.7× 480 1.0× 275 0.9× 146 0.5× 159 0.9× 26 1.6k
Shan Huang China 23 308 0.5× 306 0.6× 268 0.9× 302 1.1× 74 0.4× 80 1.8k
Andrey Zharkikh United States 22 1.3k 2.2× 640 1.3× 485 1.6× 299 1.1× 112 0.6× 50 2.2k
Thomas Mailund Denmark 24 953 1.6× 1.2k 2.4× 159 0.5× 238 0.9× 41 0.2× 88 2.0k

Countries citing papers authored by Yu Fan

Since Specialization
Citations

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

Fields of papers citing papers by Yu Fan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yu Fan

This figure shows the co-authorship network connecting the top 25 collaborators of Yu Fan. A scholar is included among the top collaborators of Yu Fan 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 Yu Fan. Yu Fan 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
1.
Liu, Jie, et al.. (2025). Causal graph-based spatial–temporal attention network for RUL prediction of complex systems. Computers & Industrial Engineering. 201. 110892–110892. 2 indexed citations
2.
Shen, Chengji, Yu Fan, & Chunhui Lu. (2024). Effects of a subsurface dam on groundwater flow and salt transport in stratified coastal aquifers: Experiments and simulations. Journal of Hydrology. 646. 132327–132327.
3.
Shen, Chengji, Yu Fan, Chunhui Lu, et al.. (2023). Characterization of hypersaline zones in salt marshes. Environmental Research Letters. 18(4). 44028–44028. 4 indexed citations
4.
Zhang, Liqiang, Xiangyu Wang, Mingyi Chen, Yu Fan, & Ming Li. (2022). A fractional-order model of lithium-ion batteries and multi-domain parameter identification method. Journal of Energy Storage. 50. 104595–104595. 26 indexed citations
5.
6.
Fu, Yuhua, Jingya Xu, Zhenshuang Tang, et al.. (2020). A gene prioritization method based on a swine multi-omics knowledgebase and a deep learning model. Communications Biology. 3(1). 502–502. 46 indexed citations
7.
Aggelen, Helen van, Raivo Kolde, Yu Fan, et al.. (2019). A core genome approach that enables prospective and dynamic monitoring of infectious outbreaks. Scientific Reports. 9(1). 7808–7808. 8 indexed citations
8.
Fan, Yu, Mohamed–Chaker Larabi, Faouzi Alaya Cheikh, & Christine Fernández-Maloigne. (2019). Just Noticeable Difference Model for Asymmetrically Distorted Stereoscopic Images. HAL (Le Centre pour la Communication Scientifique Directe). 1. 2277–2281. 2 indexed citations
9.
Fan, Yu, Xi Liu, Daniel Hughes, et al.. (2016). MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data. Genome biology. 17(1). 178–178. 154 indexed citations
10.
Fang, Li Tai, Pegah Tootoonchi Afshar, Aparna Chhibber, et al.. (2015). An ensemble approach to accurately detect somatic mutations using SomaticSeq. Genome biology. 16(1). 197–197. 67 indexed citations
11.
Li, Zhengzhou, et al.. (2013). Method for Robust Detection of Human Body Fall. 28(5). 580–585. 1 indexed citations
12.
Shen, Peidong, et al.. (2013). Multiplex target capture with double-stranded DNA probes. Genome Medicine. 5(5). 50–50. 13 indexed citations
13.
Lewis, Paul O., et al.. (2013). Posterior Predictive Bayesian Phylogenetic Model Selection. Systematic Biology. 63(3). 309–321. 40 indexed citations
14.
Fan, Yu. (2012). Learning Shape Metrics for Inferring the Nature of Allometry and Shape Classification.
15.
Shelton, Christian R., et al.. (2010). Continuous Time Bayesian Network Reasoning and Learning Engine. Journal of Machine Learning Research. 11(37). 1137–1140. 18 indexed citations
16.
Fan, Yu, Jing Xu, & Christian R. Shelton. (2010). Importance Sampling for Continuous Time Bayesian Networks. Journal of Machine Learning Research. 11(72). 2115–2140. 26 indexed citations
17.
Xie, Wangang, Paul O. Lewis, Yu Fan, Lynn Kuo, & Ming‐Hui Chen. (2010). Improving Marginal Likelihood Estimation for Bayesian Phylogenetic Model Selection. Systematic Biology. 60(2). 150–160. 773 indexed citations breakdown →
18.
Fan, Yu & Christian R. Shelton. (2008). Sampling for Approximate Inference in Continuous Time Bayesian Networks.. 30 indexed citations
19.
Fan, Yu, et al.. (2008). Estimation in the Mixed Linear Models and QR Decomposition.
20.
Fan, Yu & Changsheng Liu. (1998). IMPROVED MAN-COMPUTER INTERACTIVE CLASSIFICATION OF CLOUDS BASED ON BISPECTRAL SATELLITE IMAGERY. 12(3). 361–375. 1 indexed citations

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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