Yanyi Chu

1.3k total citations
20 papers, 912 citations indexed

About

Yanyi Chu is a scholar working on Molecular Biology, Computational Theory and Mathematics and Cancer Research. According to data from OpenAlex, Yanyi Chu has authored 20 papers receiving a total of 912 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 7 papers in Computational Theory and Mathematics and 4 papers in Cancer Research. Recurrent topics in Yanyi Chu's work include Machine Learning in Bioinformatics (7 papers), Computational Drug Discovery Methods (7 papers) and MicroRNA in disease regulation (3 papers). Yanyi Chu is often cited by papers focused on Machine Learning in Bioinformatics (7 papers), Computational Drug Discovery Methods (7 papers) and MicroRNA in disease regulation (3 papers). Yanyi Chu collaborates with scholars based in China, United States and Canada. Yanyi Chu's co-authors include Dong‐Qing Wei, Yi Xiong, Yanjing Wang, Dennis R. Salahub, Xiaoqi Shan, Qiankun Wang, Xiangeng Wang, Yufang Zhang, Mingming Jiang and Aman Chandra Kaushik and has published in prestigious journals such as IEEE Access, Nature Chemical Biology and Frontiers in Microbiology.

In The Last Decade

Yanyi Chu

20 papers receiving 902 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yanyi Chu China 15 696 383 140 90 72 20 912
Konda Mani Saravanan India 19 648 0.9× 318 0.8× 120 0.9× 42 0.5× 46 0.6× 95 1.1k
Jiayi Yin China 20 768 1.1× 289 0.8× 62 0.4× 136 1.5× 52 0.7× 41 1.2k
Minjie Mou China 14 664 1.0× 284 0.7× 72 0.5× 123 1.4× 49 0.7× 30 941
Fangping Wan United States 13 847 1.2× 523 1.4× 162 1.2× 18 0.2× 73 1.0× 18 1.1k
Shuyu Zheng China 14 600 0.9× 246 0.6× 62 0.4× 115 1.3× 57 0.8× 36 1.0k
Tunca Doğan Türkiye 13 859 1.2× 615 1.6× 220 1.6× 18 0.2× 38 0.5× 28 1.2k
Joel P. Arrais Portugal 16 485 0.7× 297 0.8× 125 0.9× 65 0.7× 22 0.3× 73 866
Yang Qiu China 13 509 0.7× 503 1.3× 159 1.1× 58 0.6× 34 0.5× 34 804
Elif Özkırımlı Türkiye 16 459 0.7× 88 0.2× 83 0.6× 17 0.2× 73 1.0× 43 775

Countries citing papers authored by Yanyi Chu

Since Specialization
Citations

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

Fields of papers citing papers by Yanyi Chu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yanyi Chu

This figure shows the co-authorship network connecting the top 25 collaborators of Yanyi Chu. A scholar is included among the top collaborators of Yanyi Chu 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 Yanyi Chu. Yanyi Chu 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.
Wang, Jianmin, Xun Wang, Yanyi Chu, et al.. (2024). Exploring the Conformational Ensembles of Protein–Protein Complex with Transformer-Based Generative Model. Journal of Chemical Theory and Computation. 20(11). 4469–4480. 8 indexed citations
2.
3.
Mao, Xueying, Yanyi Chu, & Dong‐Qing Wei. (2024). Designed with interactome-based deep learning. Nature Chemical Biology. 20(11). 1399–1401. 2 indexed citations
4.
Chu, Yanyi, Dan Yu, Kaixuan Huang, et al.. (2024). A 5′ UTR language model for decoding untranslated regions of mRNA and function predictions. Nature Machine Intelligence. 6(4). 449–460. 50 indexed citations
5.
Jiang, Xue, Aamir Mehmood, Qiankun Wang, et al.. (2023). A Self-attention Graph Convolutional Network for Precision Multi-tumor Early Diagnostics with DNA Methylation Data. Interdisciplinary Sciences Computational Life Sciences. 15(3). 405–418. 5 indexed citations
6.
Chen, Junwei, Bowen Zhao, Xueying Mao, et al.. (2023). TEPCAM: Prediction of T‐cell receptor–epitope binding specificity via interpretable deep learning. Protein Science. 33(1). e4841–e4841. 17 indexed citations
7.
Wang, Jianmin, Yanyi Chu, Jiashun Mao, et al.. (2022). De novo molecular design with deep molecular generative models for PPI inhibitors. Briefings in Bioinformatics. 23(4). 42 indexed citations
8.
Chu, Yanyi, Qiankun Wang, Lingfeng Zhang, et al.. (2022). A transformer-based model to predict peptide–HLA class I binding and optimize mutated peptides for vaccine design. Nature Machine Intelligence. 4(3). 300–311. 112 indexed citations
9.
Jiang, Mingming, Bowen Zhao, Qiankun Wang, et al.. (2021). NeuroPpred-Fuse: an interpretable stacking model for prediction of neuropeptides by fusing sequence information and feature selection methods. Briefings in Bioinformatics. 22(6). 42 indexed citations
10.
Chu, Yanyi, Zhiqi Li, Xueying Mao, et al.. (2021). MDA-CF: Predicting MiRNA-Disease associations based on a cascade forest model by fusing multi-source information. Computers in Biology and Medicine. 136. 104706–104706. 27 indexed citations
11.
Wang, Yanjing, Lingfeng Zhang, Yanyi Chu, et al.. (2021). MDF-SA-DDI: predicting drug–drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism. Briefings in Bioinformatics. 23(1). 126 indexed citations
12.
Chu, Yanyi, Xuhong Wang, Yanjing Wang, et al.. (2021). MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph. Briefings in Bioinformatics. 22(6). 58 indexed citations
13.
Chu, Yanyi, Xiaoqi Shan, Mingming Jiang, et al.. (2020). DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method. Briefings in Bioinformatics. 22(3). 64 indexed citations
14.
Wang, Xiangeng, Yanyi Chu, Yanjing Wang, et al.. (2020). T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting Algorithm. Frontiers in Microbiology. 11. 580382–580382. 32 indexed citations
15.
Zhang, Yufang, Xiangeng Wang, Aman Chandra Kaushik, et al.. (2020). SPVec: A Word2vec-Inspired Feature Representation Method for Drug-Target Interaction Prediction. Frontiers in Chemistry. 7. 895–895. 48 indexed citations
16.
17.
Shan, Xiaoqi, Xiangeng Wang, Yanyi Chu, et al.. (2019). Prediction of CYP450 Enzyme–Substrate Selectivity Based on the Network-Based Label Space Division Method. Journal of Chemical Information and Modeling. 59(11). 4577–4586. 52 indexed citations
18.
Liu, Mengyang, Yanyi Chu, Huan Liu, et al.. (2019). Accelerated Blood Clearance of Nanoemulsions Modified with PEG-Cholesterol and PEG-Phospholipid Derivatives in Rats: The Effect of PEG-Lipid Linkages and PEG Molecular Weights. Molecular Pharmaceutics. 17(4). 1059–1070. 39 indexed citations
19.
Chu, Yanyi, Aman Chandra Kaushik, Xiangeng Wang, et al.. (2019). DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features. Briefings in Bioinformatics. 22(1). 451–462. 158 indexed citations
20.
Chu, Yanyi, et al.. (2016). Separation and characterization of benzaldehyde-functional polyethylene glycols by liquid chromatography under critical conditions. Polymer Chemistry. 7(48). 7506–7513. 5 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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