Wei Peng

2.8k total citations · 1 hit paper
96 papers, 1.9k citations indexed

About

Wei Peng is a scholar working on Molecular Biology, Computational Theory and Mathematics and Cancer Research. According to data from OpenAlex, Wei Peng has authored 96 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 70 papers in Molecular Biology, 24 papers in Computational Theory and Mathematics and 18 papers in Cancer Research. Recurrent topics in Wei Peng's work include Bioinformatics and Genomic Networks (45 papers), Machine Learning in Bioinformatics (24 papers) and Computational Drug Discovery Methods (23 papers). Wei Peng is often cited by papers focused on Bioinformatics and Genomic Networks (45 papers), Machine Learning in Bioinformatics (24 papers) and Computational Drug Discovery Methods (23 papers). Wei Peng collaborates with scholars based in China, United States and Canada. Wei Peng's co-authors include Jianxin Wang, Wei Dai, Fang‐Xiang Wu, Wei Lan, Yi Pan, Jiancheng Zhong, Tielin Chen, Xiaoqing Peng, Min Li and Feng Wang and has published in prestigious journals such as Bioinformatics, Scientific Reports and International Journal of Molecular Sciences.

In The Last Decade

Wei Peng

86 papers receiving 1.9k citations

Hit Papers

align trajectories

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.

The Large Language Models on Biomedical Data Analysis: A Survey

2025 17 citations Wei Lan, Wei Peng et al. IEEE Journal of Biomedical and Health Informatics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Wei Peng China	27	1.4k	497	396	134	64	96	1.9k
Fei He China	17	554 0.4×	62 0.1×	72 0.2×	92 0.7×	17 0.3×	86	1.2k
Kaiyan Feng China	30	2.2k 1.5×	586 1.2×	201 0.5×	189 1.4×	1 0.0×	120	2.8k
Daniela Besozzi Italy	19	603 0.4×	229 0.5×	28 0.1×	381 2.8×	7 0.1×	85	1.3k
Giulia Fiscon Italy	23	1.1k 0.7×	274 0.6×	467 1.2×	89 0.7×		62	1.6k
Qiao Liu China	23	1.1k 0.8×	219 0.4×	195 0.5×	212 1.6×		117	1.9k
Tsung‐Hsien Lee Taiwan	27	325 0.2×	53 0.1×	83 0.2×	94 0.7×	18 0.3×	111	2.4k
Zhenqiang Su United States	21	817 0.6×	259 0.5×	244 0.6×	96 0.7×		47	1.5k
Lijun Cai China	23	1.2k 0.8×	220 0.4×	442 1.1×	128 1.0×		61	1.6k
Daichi Shigemizu Japan	23	879 0.6×	131 0.3×	191 0.5×	135 1.0×		52	1.5k
Ranadip Pal United States	25	1.5k 1.1×	509 1.0×	90 0.2×	146 1.1×	1 0.0×	98	2.2k

Countries citing papers authored by Wei Peng

Since Specialization

Citations

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

Fields of papers citing papers by Wei Peng

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Wei Peng

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

All Works

Sort: Min cites: Since: Top N: Style:

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.

Pan, Yu, et al.. (2024). Chest radiology report generation based on cross-modal multi-scale feature fusion. Journal of Radiation Research and Applied Sciences. 17(1). 100823–100823. 5 indexed citations

Fu, Xiaodong, et al.. (2024). Multi-domains personalized local differential privacy frequency estimation mechanism for utility optimization. Computers & Security. 150. 104273–104273.

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, 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

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

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

10.

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

11.

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

12.

Lan, Wei, Mingrui Zhu, Qingfeng Chen, et al.. (2021). Prediction of circRNA‐miRNA Associations Based on Network Embedding. Complexity. 2021(1). 19 indexed citations

13.

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

14.

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

15.

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

16.

Peng, Wei, et al.. (2019). Identifying driver genes involving gene dysregulated expression, tissue-specific expression and gene-gene network. BMC Medical Genomics. 12(S7). 168–168. 15 indexed citations

17.

Peng, Wei, et al.. (2019). A random walk-based method to identify driver genes by integrating the subcellular localization and variation frequency into bipartite graph. BMC Bioinformatics. 20(1). 238–238. 32 indexed citations

18.

Peng, Wei, et al.. (2019). An Entropy-Based Method for Identifying Mutual Exclusive Driver Genes in Cancer. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 17(3). 758–768. 31 indexed citations

19.

Peng, Wei, Wei Lan, Jiancheng Zhong, Jianxin Wang, & Yi Pan. (2017). A novel method of predicting microRNA-disease associations based on microRNA, disease, gene and environment factor networks. Methods. 124. 69–77. 25 indexed citations

20.

Peng, Wei. (2011). Research and Implementation of Human Detection Based on Extended Histograms of Oriented Gradients. Journal of Guangxi Normal University. 1 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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