Lei Deng

6.1k total citations · 3 hit papers
167 papers, 4.3k citations indexed

About

Lei Deng is a scholar working on Molecular Biology, Cancer Research and Computational Theory and Mathematics. According to data from OpenAlex, Lei Deng has authored 167 papers receiving a total of 4.3k indexed citations (citations by other indexed papers that have themselves been cited), including 140 papers in Molecular Biology, 51 papers in Cancer Research and 35 papers in Computational Theory and Mathematics. Recurrent topics in Lei Deng's work include Cancer-related molecular mechanisms research (42 papers), Machine Learning in Bioinformatics (36 papers) and Computational Drug Discovery Methods (35 papers). Lei Deng is often cited by papers focused on Cancer-related molecular mechanisms research (42 papers), Machine Learning in Bioinformatics (36 papers) and Computational Drug Discovery Methods (35 papers). Lei Deng collaborates with scholars based in China, United States and Singapore. Lei Deng's co-authors include Waltraud X. Schulze, Matthias Mann, Jingpu Zhang, Hui Liu, Qiangfeng Cliff Zhang, Donald Petrey, Barry Honig, Minghui Yang, Zhigang Chen and Jinxian Wang and has published in prestigious journals such as Nature, Nucleic Acids Research and SHILAP Revista de lepidopterología.

In The Last Decade

Lei Deng

155 papers receiving 4.2k citations

Hit Papers

Phosphotyrosine interactome of the ErbB‐receptor kinase f... 2005 2026 2012 2019 2005 2012 2025 100 200 300 400 500
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. Phosphotyrosine interactome of the ErbB‐receptor kinase family
    2005 574 citations Lei Deng et al. profile →
  2. Structure-based prediction of protein–protein interactions on a genome-wide scale
    2012 527 citations Lei Deng et al. profile →
  3. Peptide hormones in plants
    2025 20 citations Lei Deng et al. PubMed profile →

Peers

Lei Deng
Jiguang Wang China
Shanrong Zhao United States
Yanzhi Guo China
Hui Ding China
Rengül Çetin-Atalay Türkiye
Michael Fernández Cuba
Qi Liu China
Isidro Cortés‐Ciriano United Kingdom
Fuyi Li China
Jiguang Wang China
Lei Deng
Citations per year, relative to Lei Deng Lei Deng (= 1×) peers Jiguang Wang

Countries citing papers authored by Lei Deng

Since Specialization
Citations

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

Fields of papers citing papers by Lei Deng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lei Deng

This figure shows the co-authorship network connecting the top 25 collaborators of Lei Deng. A scholar is included among the top collaborators of Lei Deng 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 Lei Deng. Lei Deng 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.
Shen, Siyuan, et al.. (2025). CircGO: Predicting circRNA Functions Through Self-Supervised Learning of Heterogeneous Networks. PubMed. 22(5). 1991–2000. 1 indexed citations
2.
3.
Zhang, Pan, et al.. (2024). IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network. Briefings in Bioinformatics. 25(3). 4 indexed citations
4.
He, Yumin, Xiaoying Liu, Qingwen Gui, et al.. (2024). Peptide aptamer-based colorimetric sensor for the detection of L-tryptophan in porcine serum. Microchemical Journal. 197. 109896–109896. 12 indexed citations
5.
Zhang, Jiaxuan, et al.. (2024). DeepFusionCDR: Employing Multi-Omics Integration and Molecule-Specific Transformers for Enhanced Prediction of Cancer Drug Responses. IEEE Journal of Biomedical and Health Informatics. 28(10). 6248–6258. 2 indexed citations
6.
Huang, Zhijian, et al.. (2024). DeepRSMA: a cross-fusion-based deep learning method for RNA–small molecule binding affinity prediction. Bioinformatics. 40(12). 5 indexed citations
7.
Zhang, Jingpu, et al.. (2024). ncRNA Coding Potential Prediction Using BiLSTM and Transformer Encoder-Based Model. Journal of Chemical Information and Modeling. 64(16). 6712–6722. 2 indexed citations
8.
9.
Li, Guangdi, et al.. (2023). AntiViralDL: Computational Antiviral Drug Repurposing Using Graph Neural Network and Self-Supervised Learning. IEEE Journal of Biomedical and Health Informatics. 28(1). 548–556. 11 indexed citations
10.
Deng, Lei, et al.. (2023). Prediction of lncRNA functions using deep neural networks based on multiple networks. BMC Genomics. 23(S6). 865–865.
11.
Liu, Hui, et al.. (2022). Attention-wise masked graph contrastive learning for predicting molecular property. Briefings in Bioinformatics. 23(5). 35 indexed citations
12.
Deng, Lei, et al.. (2022). Dual-Channel Heterogeneous Graph Neural Network for Predicting microRNA-Mediated Drug Sensitivity. Journal of Chemical Information and Modeling. 62(23). 5929–5937. 14 indexed citations
13.
Wang, Jinxian, Xuejun Liu, Siyuan Shen, Lei Deng, & Hui Liu. (2021). DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations. Briefings in Bioinformatics. 23(1). 127 indexed citations
14.
Deng, Lei, et al.. (2021). Graph2MDA: a multi-modal variational graph embedding model for predicting microbe–drug associations. Bioinformatics. 38(4). 1118–1125. 54 indexed citations
15.
Deng, Lei, Wei Lin, Jiacheng Wang, & Jingpu Zhang. (2020). DeepciRGO: functional prediction of circular RNAs through hierarchical deep neural networks using heterogeneous network features. BMC Bioinformatics. 21(1). 519–519. 10 indexed citations
16.
Wang, Jiacheng, Jingpu Zhang, Yideng Cai, & Lei Deng. (2019). DeepMiR2GO: Inferring Functions of Human MicroRNAs Using a Deep Multi-Label Classification Model. International Journal of Molecular Sciences. 20(23). 6046–6046. 11 indexed citations
17.
Deng, Lei, et al.. (2019). LDAH2V: Exploring Meta-Paths Across Multiple Networks for lncRNA-Disease Association Prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 18(4). 1572–1581. 29 indexed citations
18.
Deng, Lei, et al.. (2018). Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network. BMC Bioinformatics. 19(1). 370–370. 34 indexed citations
19.
Deng, Lei, Xiaojie Xu, & Hui Liu. (2018). PredCSO: an ensemble method for the prediction of S-sulfenylation sites in proteins. Molecular Omics. 14(4). 257–265. 12 indexed citations
20.
Deng, Lei, Shengchang Su, Minlu Zhang, et al.. (2010). Investigating the predictability of essential genes across distantly related organisms using an integrative approach. Nucleic Acids Research. 39(3). 795–807. 109 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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