Kaiyan Feng

3.4k total citations
120 papers, 2.8k citations indexed

About

Kaiyan Feng is a scholar working on Molecular Biology, Computational Theory and Mathematics and Infectious Diseases. According to data from OpenAlex, Kaiyan Feng has authored 120 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 92 papers in Molecular Biology, 28 papers in Computational Theory and Mathematics and 17 papers in Infectious Diseases. Recurrent topics in Kaiyan Feng's work include Machine Learning in Bioinformatics (64 papers), Computational Drug Discovery Methods (28 papers) and Protein Structure and Dynamics (22 papers). Kaiyan Feng is often cited by papers focused on Machine Learning in Bioinformatics (64 papers), Computational Drug Discovery Methods (28 papers) and Protein Structure and Dynamics (22 papers). Kaiyan Feng collaborates with scholars based in China, United States and United Kingdom. Kaiyan Feng's co-authors include Yu‐Dong Cai, Lei Chen, Tao Huang, Kuo‐Chen Chou, Wencong Lu, Biqing Li, Le‐Le Hu, Yixue Li, Kuo‐Chen Chou and Xiangyin Kong and has published in prestigious journals such as PLoS ONE, Biochemical and Biophysical Research Communications and International Journal of Molecular Sciences.

In The Last Decade

Kaiyan Feng

115 papers receiving 2.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kaiyan Feng China 30 2.2k 586 201 189 117 120 2.8k
Jing Tang China 23 1.7k 0.8× 474 0.8× 258 1.3× 135 0.7× 77 0.7× 79 2.6k
Qingxia Yang China 25 1.9k 0.9× 496 0.8× 430 2.1× 156 0.8× 76 0.6× 59 2.8k
Yi Xiong China 32 2.2k 1.0× 807 1.4× 233 1.2× 232 1.2× 41 0.4× 126 3.1k
Yanjie Wei China 25 1.1k 0.5× 397 0.7× 262 1.3× 129 0.7× 129 1.1× 130 2.0k
Tae Hwan Shin South Korea 29 2.5k 1.1× 280 0.5× 191 1.0× 114 0.6× 56 0.5× 58 3.3k
Md Mehedi Hasan Japan 34 2.6k 1.2× 258 0.4× 131 0.7× 111 0.6× 56 0.5× 96 3.0k
Bingding Huang China 18 1.1k 0.5× 543 0.9× 114 0.6× 136 0.7× 69 0.6× 59 2.1k
Amitabh Sharma United States 22 1.9k 0.9× 546 0.9× 185 0.9× 170 0.9× 79 0.7× 38 3.0k
Pankaj Agarwal United States 22 1.8k 0.8× 853 1.5× 107 0.5× 141 0.7× 45 0.4× 81 2.6k
Joe G. Greener United Kingdom 12 989 0.5× 251 0.4× 101 0.5× 156 0.8× 47 0.4× 19 1.9k

Countries citing papers authored by Kaiyan Feng

Since Specialization
Citations

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

Fields of papers citing papers by Kaiyan Feng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kaiyan Feng

This figure shows the co-authorship network connecting the top 25 collaborators of Kaiyan Feng. A scholar is included among the top collaborators of Kaiyan Feng 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 Kaiyan Feng. Kaiyan Feng 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.
Yuan, Fei, et al.. (2025). Integrative multi-omics machine learning reveals novel driver genes associations in lung adenocarcinoma. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics. 1874(1). 141113–141113.
2.
3.
Shen, Yulong, et al.. (2024). Machine Learning Reveals Impacts of Smoking on Gene Profiles of Different Cell Types in Lung. Life. 14(4). 502–502. 3 indexed citations
4.
Zhang, Yuhang, et al.. (2024). Machine Learning in Identifying Marker Genes for Congenital Heart Diseases of Different Cardiac Cell Types. Life. 14(8). 1032–1032. 1 indexed citations
5.
Guo, Wei, et al.. (2024). Identifying Autophagy-Associated Proteins and Chemicals with a Random Walk-Based Method within Heterogeneous Interaction Network. Frontiers in Bioscience-Landmark. 29(1). 21–21. 3 indexed citations
6.
Yuan, Fei, Wei Guo, Lei Chen, et al.. (2023). Identification of Whole-Blood DNA Methylation Signatures and Rules Associated with COVID-19 Severity. Frontiers in Bioscience-Landmark. 28(11). 284–284. 2 indexed citations
7.
Zhang, Yuhang, Wei Guo, Kaiyan Feng, et al.. (2023). Identification of Genes Associated with the Impairment of Olfactory and Gustatory Functions in COVID-19 via Machine-Learning Methods. Life. 13(3). 798–798. 20 indexed citations
8.
Li, Jing, et al.. (2023). Identification of dynamic gene expression profiles during sequential vaccination with ChAdOx1/BNT162b2 using machine learning methods. Frontiers in Microbiology. 14. 1138674–1138674. 2 indexed citations
9.
Guo, Wei, Kaiyan Feng, Lin Zhu, et al.. (2023). Characterization of chromatin accessibility patterns in different mouse cell types using machine learning methods at single-cell resolution. Frontiers in Genetics. 14. 1145647–1145647. 3 indexed citations
10.
Feng, Kaiyan, Yizhong Li, Sanxin Liu, et al.. (2023). High homocysteine is associated with idiopathic normal pressure hydrocephalus in deep perforating arteriopathy: a cross-sectional study. BMC Geriatrics. 23(1). 382–382. 2 indexed citations
11.
12.
Chen, Lei, et al.. (2023). Identification of key gene expression associated with quality of life after recovery from COVID-19. Medical & Biological Engineering & Computing. 62(4). 1031–1048. 14 indexed citations
13.
Chen, Lei, Xiaoyong Pan, Yuhang Zhang, et al.. (2019). Primary Tumor Site Specificity is Preserved in Patient-Derived Tumor Xenograft Models. Frontiers in Genetics. 10. 738–738. 12 indexed citations
14.
Chen, Lei, Chen Chu, & Kaiyan Feng. (2015). Predicting the types of metabolic pathway of compounds using molecular fragments and sequential minimal optimization. Combinatorial Chemistry & High Throughput Screening. 19(2). 136–143. 37 indexed citations
15.
Cui, Weiren, Lei Chen, Tao Huang, et al.. (2013). Computationally identifying virulence factors based on KEGG pathways. Molecular BioSystems. 9(6). 1447–1452. 22 indexed citations
16.
Chen, Lei, Tao Huang, Jian Zhang, et al.. (2013). Predicting Drugs Side Effects Based on Chemical-Chemical Interactions and Protein-Chemical Interactions. BioMed Research International. 2013. 1–8. 23 indexed citations
17.
Gao, Yufei, et al.. (2012). Prediction of active sites of enzymes by maximum relevance minimum redundancy (mRMR) feature selection. Molecular BioSystems. 9(1). 61–69. 29 indexed citations
18.
Huang, Tao, Xiaohe Shi, Ping Wang, et al.. (2010). Analysis and Prediction of the Metabolic Stability of Proteins Based on Their Sequential Features, Subcellular Locations and Interaction Networks. PLoS ONE. 5(6). e10972–e10972. 129 indexed citations
19.
Cai, Yu‐Dong, Jianfeng He, Xinlei Li, et al.. (2010). Prediction of Protein Subcellular Locations with Feature Selection and Analysis. Protein and Peptide Letters. 17(4). 464–472. 26 indexed citations
20.
Cai, Yu‐Dong, Kaiyan Feng, Wencong Lu, & Kuo‐Chen Chou. (2005). Using LogitBoost classifier to predict protein structural classes. Journal of Theoretical Biology. 238(1). 172–176. 172 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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