Hai-Cheng Yi

1.6k total citations
39 papers, 1000 citations indexed

About

Hai-Cheng Yi is a scholar working on Molecular Biology, Computational Theory and Mathematics and Cancer Research. According to data from OpenAlex, Hai-Cheng Yi has authored 39 papers receiving a total of 1000 indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Molecular Biology, 18 papers in Computational Theory and Mathematics and 13 papers in Cancer Research. Recurrent topics in Hai-Cheng Yi's work include Computational Drug Discovery Methods (18 papers), Machine Learning in Bioinformatics (16 papers) and Bioinformatics and Genomic Networks (14 papers). Hai-Cheng Yi is often cited by papers focused on Computational Drug Discovery Methods (18 papers), Machine Learning in Bioinformatics (16 papers) and Bioinformatics and Genomic Networks (14 papers). Hai-Cheng Yi collaborates with scholars based in China, Hong Kong and Singapore. Hai-Cheng Yi's co-authors include Zhu‐Hong You, Zhan‐Heng Chen, De-Shuang Huang, Zhen-Hao Guo, Tonghai Jiang, Chee Keong Kwoh, Xi Zhou, Yanbin Wang, Liping Li and Li Cheng and has published in prestigious journals such as International Journal of Molecular Sciences, Molecules and BMC Bioinformatics.

In The Last Decade

Hai-Cheng Yi

37 papers receiving 991 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hai-Cheng Yi China 17 804 362 248 105 79 39 1000
Xiangzheng Fu China 21 1.1k 1.3× 325 0.9× 323 1.3× 124 1.2× 98 1.2× 84 1.3k
Jianbo Fu China 17 806 1.0× 223 0.6× 185 0.7× 90 0.9× 30 0.4× 28 1.2k
Fei Guo China 13 737 0.9× 252 0.7× 73 0.3× 95 0.9× 47 0.6× 38 972
Yanyi Chu China 15 696 0.9× 383 1.1× 90 0.4× 63 0.6× 140 1.8× 20 912
Pu-Feng Du China 23 1.7k 2.1× 207 0.6× 159 0.6× 126 1.2× 14 0.2× 60 2.0k
Hao Lv China 30 2.0k 2.5× 198 0.5× 275 1.1× 117 1.1× 32 0.4× 65 2.3k
Len Trigg New Zealand 5 735 0.9× 100 0.3× 178 0.7× 105 1.0× 22 0.3× 8 1.1k
Xiaorui Su China 18 769 1.0× 549 1.5× 144 0.6× 156 1.5× 113 1.4× 43 1.0k
Wang‐Ren Qiu China 22 2.7k 3.4× 309 0.9× 170 0.7× 80 0.8× 18 0.2× 68 2.9k
Shixiang Wan China 9 641 0.8× 86 0.2× 147 0.6× 110 1.0× 30 0.4× 12 842

Countries citing papers authored by Hai-Cheng Yi

Since Specialization
Citations

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

Fields of papers citing papers by Hai-Cheng Yi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hai-Cheng Yi. 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 Hai-Cheng Yi. The network helps show where Hai-Cheng Yi may publish in the future.

Co-authorship network of co-authors of Hai-Cheng Yi

This figure shows the co-authorship network connecting the top 25 collaborators of Hai-Cheng Yi. A scholar is included among the top collaborators of Hai-Cheng Yi 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 Hai-Cheng Yi. Hai-Cheng Yi 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.
Chen, Shihong, Hai-Cheng Yi, Zhu‐Hong You, et al.. (2025). Local–Global Structure-Aware Geometric Equivariant Graph Representation Learning for Predicting Protein–Ligand Binding Affinity. IEEE Transactions on Neural Networks and Learning Systems. 36(8). 15181–15193. 1 indexed citations
2.
Yi, Hai-Cheng, et al.. (2025). Three-dimensional geometric deep learning for reaction prediction with equivariant graph transformer. Engineering Applications of Artificial Intelligence. 163. 112850–112850.
3.
Yi, Hai-Cheng, et al.. (2024). MathEagle: Accurate prediction of drug-drug interaction events via multi-head attention and heterogeneous attribute graph learning. Computers in Biology and Medicine. 177. 108642–108642. 6 indexed citations
4.
You, Zhu‐Hong, et al.. (2024). Integrated Knowledge Graph and Drug Molecular Graph Fusion via Adversarial Networks for Drug–Drug Interaction Prediction. Journal of Chemical Information and Modeling. 64(21). 8361–8372. 1 indexed citations
5.
Yun, Lijun, et al.. (2024). Accurate prediction of drug combination risk levels based on relational graph convolutional network and multi-head attention. Journal of Translational Medicine. 22(1). 572–572. 10 indexed citations
6.
You, Zhu‐Hong, et al.. (2024). MRGCDDI: Multi-Relation Graph Contrastive Learning Without Data Augmentation for Drug-Drug Interaction Events Prediction. IEEE Journal of Biomedical and Health Informatics. 30(2). 1735–1745. 2 indexed citations
7.
Jia, Zheng, Hai-Cheng Yi, & Zhu‐Hong You. (2024). Equivariant 3D-Conditional Diffusion Model for De Novo Drug Design. IEEE Journal of Biomedical and Health Informatics. 29(3). 1805–1816. 7 indexed citations
8.
Yun, Lijun, et al.. (2024). Fusing graph transformer with multi-aggregate GCN for enhanced drug–disease associations prediction. BMC Bioinformatics. 25(1). 79–79. 9 indexed citations
9.
Su, Xiaorui, Pengwei Hu, Hai-Cheng Yi, Zhu‐Hong You, & Lun Hu. (2022). Predicting Drug-Target Interactions Over Heterogeneous Information Network. IEEE Journal of Biomedical and Health Informatics. 27(1). 562–572. 40 indexed citations
10.
Guo, Zhen-Hao, Zhu‐Hong You, De-Shuang Huang, et al.. (2020). MeSHHeading2vec: a new method for representing MeSH headings as vectors based on graph embedding algorithm. Briefings in Bioinformatics. 22(2). 2085–2095. 32 indexed citations
11.
Wong, Leon, et al.. (2020). MIPDH: A Novel Computational Model for Predicting microRNA–mRNA Interactions by DeepWalk on a Heterogeneous Network. ACS Omega. 5(28). 17022–17032. 11 indexed citations
12.
Guo, Zhen-Hao, Zhu‐Hong You, Yanbin Wang, et al.. (2020). Bioentity2vec: Attribute- and behavior-driven representation for predicting multi-type relationships between bioentities. GigaScience. 9(6). 2 indexed citations
13.
Li, Jianqiang, Zhu‐Hong You, Hai-Cheng Yi, et al.. (2020). Using Weighted Extreme Learning Machine Combined With Scale-Invariant Feature Transform to Predict Protein-Protein Interactions From Protein Evolutionary Information. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 17(5). 1546–1554. 16 indexed citations
14.
Yi, Hai-Cheng, Zhu‐Hong You, Zhen-Hao Guo, De-Shuang Huang, & Keith C. C. Chan. (2020). Learning Representation of Molecules in Association Network for Predicting Intermolecular Associations. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 18(6). 2546–2554. 15 indexed citations
15.
Chen, Zhan‐Heng, et al.. (2020). Prediction of Drug–Target Interactions From Multi-Molecular Network Based on Deep Walk Embedding Model. Frontiers in Bioengineering and Biotechnology. 8. 338–338. 38 indexed citations
16.
17.
Chen, Zhan‐Heng, Zhu‐Hong You, Liping Li, et al.. (2019). Prediction of Self-Interacting Proteins from Protein Sequence Information Based on Random Projection Model and Fast Fourier Transform. International Journal of Molecular Sciences. 20(4). 930–930. 17 indexed citations
18.
Jia, Lina, et al.. (2019). BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information. International Journal of Molecular Sciences. 20(4). 978–978. 15 indexed citations
19.
Yi, Hai-Cheng, Zhu‐Hong You, Xi Zhou, et al.. (2019). ACP-DL: A Deep Learning Long Short-Term Memory Model to Predict Anticancer Peptides Using High-Efficiency Feature Representation. Molecular Therapy — Nucleic Acids. 17. 1–9. 160 indexed citations
20.
Yi, Hai-Cheng, Zhu‐Hong You, De-Shuang Huang, et al.. (2018). A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information. Molecular Therapy — Nucleic Acids. 11. 337–344. 96 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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