Wei Dai

1.1k total citations
61 papers, 675 citations indexed

About

Wei Dai is a scholar working on Molecular Biology, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Wei Dai has authored 61 papers receiving a total of 675 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Molecular Biology, 11 papers in Artificial Intelligence and 11 papers in Computational Theory and Mathematics. Recurrent topics in Wei Dai's work include Bioinformatics and Genomic Networks (20 papers), Gene expression and cancer classification (15 papers) and Machine Learning in Bioinformatics (11 papers). Wei Dai is often cited by papers focused on Bioinformatics and Genomic Networks (20 papers), Gene expression and cancer classification (15 papers) and Machine Learning in Bioinformatics (11 papers). Wei Dai collaborates with scholars based in China, United States and Greece. Wei Dai's co-authors include Wei Peng, Wei Ji, Tielin Chen, Wei Lan, Jianxin Wang, Shoulin Wei, Hancheng Liu, Yu Ning, Bo Liang and Xiaodong Fu and has published in prestigious journals such as Bioinformatics, The Astrophysical Journal and Monthly Notices of the Royal Astronomical Society.

In The Last Decade

Wei Dai

52 papers receiving 646 citations

Peers

Wei Dai

CTM

Fantine Mordelet France

Robert Mah United States

Loïc Paulevé France

Tianyi Zang China

Vincenzo Bonnici Italy

Hongqiang Wang China

Hanyu Zhang China

Turki Turki Saudi Arabia

Noël Malod‐Dognin United Kingdom

Jingyu Hou China

Fantine Mordelet France

Wei Dai

560 ×1.5

69 ×0.5

CTM

159 ×1.1

29 ×0.3

21 ×0.3

Citations per year, relative to Wei Dai Wei Dai (= 1×) peers Fantine Mordelet

Countries citing papers authored by Wei Dai

Since Specialization

Citations

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

Fields of papers citing papers by Wei Dai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Wei Dai

This figure shows the co-authorship network connecting the top 25 collaborators of Wei Dai. A scholar is included among the top collaborators of Wei Dai 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 Wei Dai. Wei Dai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Dai, Wei, Chen Gong, Wei Peng, et al.. (2025). Domain alignment method based on masked variational autoencoder for predicting patient anticancer drug response. Methods. 238. 61–73.

Peng, Wei, Xinyue Xu, Chen Gong, et al.. (2025). Predicting Anti-Cancer Drug Response Based on Hypergraph Representation Learning. PubMed. 22(6). 2430–2441.

Peng, Wei, et al.. (2025). Integrating Time and Frequency Domain Features of fMRI Time Series for Alzheimer’s Disease Classification Using Graph Neural Networks. Interdisciplinary Sciences Computational Life Sciences. 1 indexed citations

Li, Zuzheng, Jian‐Gang Zhu, Yanzheng Yang, et al.. (2024). Scale effect of landscape characteristics on undergrowth vegetation variance with different ecological traits. Ecological Frontiers. 44(6). 1269–1279.

Hao, Longfei, et al.. (2024). Radio frequency interference detection using efficient multiscale convolutional attention UNet. Monthly Notices of the Royal Astronomical Society. 529(4). 4719–4727. 5 indexed citations

Peng, Wei, et al.. (2024). Multi-Network Graph Contrastive Learning for Cancer Driver Gene Identification. IEEE Transactions on Network Science and Engineering. 11(4). 3430–3440. 15 indexed citations

Peng, Wei, et al.. (2024). Hierarchical Graph Representation Learning With Multi-Granularity Features for Anti-Cancer Drug Response Prediction. IEEE Journal of Biomedical and Health Informatics. 29(11). 7839–7850. 3 indexed citations

Peng, Wei, Wei Dai, Shoulin Wei, et al.. (2024). Supervised graph contrastive learning for cancer subtype identification through multi-omics data integration. Health Information Science and Systems. 12(1). 12–12. 7 indexed citations

Wei, Shoulin, et al.. (2024). A Study of Classroom Behavior Recognition Incorporating Super-Resolution and Target Detection. Sensors. 24(17). 5640–5640. 3 indexed citations

10.

Peng, Wei, Tielin Chen, Hancheng Liu, et al.. (2023). Improving drug response prediction based on two-space graph convolution. Computers in Biology and Medicine. 158. 106859–106859. 20 indexed citations

11.

Peng, Wei, Rong Wu, Wei Dai, et al.. (2023). MiRNA–gene network embedding for predicting cancer driver genes. Briefings in Functional Genomics. 22(4). 341–350. 11 indexed citations

12.

Peng, Wei, Hancheng Liu, Wei Dai, Yu Ning, & Jianxin Wang. (2022). Predicting cancer drug response using parallel heterogeneous graph convolutional networks with neighborhood interactions. Bioinformatics. 38(19). 4546–4553. 37 indexed citations

13.

Peng, Wei, et al.. (2022). Predicting miRNA-Disease Associations From miRNA-Gene-Disease Heterogeneous Network With Multi-Relational Graph Convolutional Network Model. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 20(6). 3363–3375. 32 indexed citations

14.

Li, Xu, Yunfei Yang, Yihua Yan, et al.. (2021). Research on Multiwavelength Isolated Bright Points Based on Deep Learning. The Astrophysical Journal. 911(1). 32–32. 5 indexed citations

15.

Peng, Wei, et al.. (2021). Improving cancer driver gene identification using multi-task learning on graph convolutional network. Briefings in Bioinformatics. 23(1). 78 indexed citations

16.

Peng, Wei, Tielin Chen, & Wei Dai. (2021). Predicting Drug Response Based on Multi-Omics Fusion and Graph Convolution. IEEE Journal of Biomedical and Health Informatics. 26(3). 1384–1393. 72 indexed citations

17.

Dai, Wei, Wenhao Yue, Wei Peng, et al.. (2021). Identifying Cancer Subtypes Using a Residual Graph Convolution Model on a Sample Similarity Network. Genes. 13(1). 65–65. 13 indexed citations

18.

Peng, Wei, et al.. (2021). Multi-View Feature Aggregation for Predicting Microbe-Disease Association. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 20(5). 2748–2758. 9 indexed citations

19.

Dai, Wei, et al.. (2020). Network Embedding the Protein–Protein Interaction Network for Human Essential Genes Identification. Genes. 11(2). 153–153. 24 indexed citations

20.

Zhang, Fengyu, et al.. (2019). A Novel Method for Identifying Essential Genes by Fusing Dynamic Protein–Protein Interactive Networks. Genes. 10(1). 31–31. 25 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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