Peiliang Wu

897 total citations
35 papers, 690 citations indexed

About

Peiliang Wu is a scholar working on Radiology, Nuclear Medicine and Imaging, Molecular Biology and Artificial Intelligence. According to data from OpenAlex, Peiliang Wu has authored 35 papers receiving a total of 690 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Radiology, Nuclear Medicine and Imaging, 10 papers in Molecular Biology and 9 papers in Artificial Intelligence. Recurrent topics in Peiliang Wu's work include COVID-19 diagnosis using AI (7 papers), Machine Learning and ELM (4 papers) and Pulmonary Hypertension Research and Treatments (4 papers). Peiliang Wu is often cited by papers focused on COVID-19 diagnosis using AI (7 papers), Machine Learning and ELM (4 papers) and Pulmonary Hypertension Research and Treatments (4 papers). Peiliang Wu collaborates with scholars based in China, Iran and Taiwan. Peiliang Wu's co-authors include Liangxing Wang, Mayun Chen, Xiaoying Huang, Huiling Chen, Ali Asghar Heidari, Hua Ye, Yanfan Chen, Dan Yao, Xiaoying Huang and Ali Chen and has published in prestigious journals such as International Journal of Molecular Sciences, IEEE Access and Life Sciences.

In The Last Decade

Peiliang Wu

30 papers receiving 683 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Peiliang Wu China 14 169 157 155 85 64 35 690
Md Habibur Rahman Bangladesh 21 369 2.2× 180 1.1× 96 0.6× 97 1.1× 61 1.0× 111 1.2k
Xueding Cai China 14 144 0.9× 143 0.9× 132 0.9× 30 0.4× 60 0.9× 29 562
Hak‐Soo Kim South Korea 13 188 1.1× 86 0.5× 119 0.8× 163 1.9× 45 0.7× 66 744
Yiqin Wang China 17 258 1.5× 103 0.7× 99 0.6× 36 0.4× 59 0.9× 133 1.1k
Ancong Wu China 7 204 1.2× 68 0.4× 121 0.8× 33 0.4× 63 1.0× 10 1.6k
Mayun Chen China 20 332 2.0× 230 1.5× 247 1.6× 151 1.8× 150 2.3× 34 1.3k
Khoa Tran Vietnam 7 189 1.1× 181 1.2× 147 0.9× 232 2.7× 30 0.5× 20 721
Haiyue Wang China 15 273 1.6× 85 0.5× 155 1.0× 69 0.8× 114 1.8× 50 727
Vanathi Gopalakrishnan United States 18 455 2.7× 191 1.2× 66 0.4× 75 0.9× 49 0.8× 52 1.0k

Countries citing papers authored by Peiliang Wu

Since Specialization
Citations

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

Fields of papers citing papers by Peiliang Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peiliang Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Peiliang Wu. A scholar is included among the top collaborators of Peiliang Wu 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 Peiliang Wu. Peiliang Wu 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
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Xing, Jie, et al.. (2024). Optimized fuzzy K-nearest neighbor approach for accurate lung cancer prediction based on radial endobronchial ultrasonography. Computers in Biology and Medicine. 171. 108038–108038. 12 indexed citations
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Liu, Yining, et al.. (2024). An enhanced machine learning-based prognostic prediction model for patients with AECOPD on invasive mechanical ventilation. iScience. 27(12). 111230–111230. 1 indexed citations
5.
Liu, Pei‐Kang, Winston Lee, Eugene Yu‐Chuan Kang, et al.. (2024). Cross-Sectional Analysis of Outer Retinal Tubulation in Inherited Retinal Diseases: A Multicenter Study. American Journal of Ophthalmology. 269. 116–135.
6.
Li, Yu‐Peng, Yining Liu, Dong Zhao, et al.. (2023). An optimized machine learning method for predicting wogonin therapy for the treatment of pulmonary hypertension. Computers in Biology and Medicine. 164. 107293–107293. 6 indexed citations
7.
Wu, Peiliang, Eugene Yu‐Chuan Kang, Pei‐Hsuan Lin, et al.. (2023). Clinical Characteristics and Genetic Variants in Taiwanese Patients With PROM1-Related Inherited Retinal Disorders. Investigative Ophthalmology & Visual Science. 64(14). 25–25. 2 indexed citations
8.
Kang, Eugene Yu‐Chuan, et al.. (2023). The Value of Electroretinography in Identifying Candidate Genes for Inherited Retinal Dystrophies: A Diagnostic Guide. Diagnostics. 13(19). 3041–3041. 5 indexed citations
9.
Wang, Mingjing, Siyuan Chen, Ali Asghar Heidari, et al.. (2022). An evolutionary machine learning for pulmonary hypertension animal model from arterial blood gas analysis. Computers in Biology and Medicine. 146. 105529–105529. 20 indexed citations
10.
Su, Hang, Dong Zhao, Fanhua Yu, et al.. (2022). Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques. Frontiers in Neuroinformatics. 16. 1029690–1029690. 5 indexed citations
11.
Hu, Jiao, et al.. (2022). Identification of Pulmonary Hypertension Animal Models Using a New Evolutionary Machine Learning Framework Based on Blood Routine Indicators. Journal of Bionic Engineering. 20(2). 762–781. 10 indexed citations
12.
Su, Hang, Dong Zhao, Ali Asghar Heidari, et al.. (2022). A new machine learning model for predicting severity prognosis in patients with pulmonary embolism: Study protocol from Wenzhou, China. Frontiers in Neuroinformatics. 16. 1052868–1052868. 2 indexed citations
13.
Ye, Hua, Peiliang Wu, Zhongxiang Xiao, et al.. (2021). Diagnosing Coronavirus Disease 2019 (COVID-19): Efficient Harris Hawks-Inspired Fuzzy K-Nearest Neighbor Prediction Methods. IEEE Access. 9. 17787–17802. 49 indexed citations
14.
Wu, Peiliang, Mayun Chen, Junwei Sun, et al.. (2020). Elucidation of the Mechanisms and Molecular Targets of Qishen Yiqi Formula for the Treatment of Pulmonary Arterial Hypertension using a Bioinformatics/Network Topology-based Strategy. Combinatorial Chemistry & High Throughput Screening. 24(5). 701–715. 8 indexed citations
15.
Wan, Tingting, Peiliang Wu, Hailong Wang, et al.. (2020). Recurrent pneumothorax and intrapulmonary cavitary lesions in a male patient with vascular Ehlers-Danlos syndrome and a novel missense mutation in the COL3A1 gene: a case report. BMC Pulmonary Medicine. 20(1). 149–149. 5 indexed citations
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Hsu, Wei‐Li, et al.. (2016). Neutrophil Gelatinase-Associated Lipocalin in Cats with Naturally Occurring Chronic Kidney Disease. Journal of Veterinary Internal Medicine. 31(1). 102–108. 16 indexed citations
18.
Huang, Xiaoying, Yicheng He, Yanfan Chen, et al.. (2016). Baicalin attenuates bleomycin-induced pulmonary fibrosis via adenosine A2a receptor related TGF-β1-induced ERK1/2 signaling pathway. BMC Pulmonary Medicine. 16(1). 132–132. 66 indexed citations
19.
Wang, Xiaobing, Wu Wang, Xiaochun Zhu, et al.. (2015). The potential of asiaticoside for TGF-β1/Smad signaling inhibition in prevention and progression of hypoxia-induced pulmonary hypertension. Life Sciences. 137. 56–64. 32 indexed citations
20.

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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