Peipei Ping

1.2k total citations
23 papers, 759 citations indexed

About

Peipei Ping is a scholar working on Molecular Biology, Spectroscopy and Information Systems and Management. According to data from OpenAlex, Peipei Ping has authored 23 papers receiving a total of 759 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Molecular Biology, 7 papers in Spectroscopy and 3 papers in Information Systems and Management. Recurrent topics in Peipei Ping's work include Bioinformatics and Genomic Networks (8 papers), Advanced Proteomics Techniques and Applications (7 papers) and Machine Learning in Bioinformatics (5 papers). Peipei Ping is often cited by papers focused on Bioinformatics and Genomic Networks (8 papers), Advanced Proteomics Techniques and Applications (7 papers) and Machine Learning in Bioinformatics (5 papers). Peipei Ping collaborates with scholars based in United States, China and Poland. Peipei Ping's co-authors include Jie Wang, David A. Liem, Howard Choi, Jessica M. Lee, Wei Wang, Bilal Mirza, Neo Christopher Chung, Maggie P. Y. Lam, Elizabeth Murphy and Dominic C. M. Ng and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of the American College of Cardiology and Gene.

In The Last Decade

Peipei Ping

22 papers receiving 746 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Peipei Ping United States 10 445 106 83 66 64 23 759
Gökhan Ertaylan Belgium 16 396 0.9× 75 0.7× 115 1.4× 27 0.4× 155 2.4× 39 837
Marta Iannuccelli Italy 12 1.2k 2.7× 66 0.6× 102 1.2× 32 0.5× 99 1.5× 20 1.4k
Jörg Ackermann Germany 15 588 1.3× 46 0.4× 42 0.5× 55 0.8× 35 0.5× 50 812
Konstantinos Sidiropoulos United Kingdom 9 650 1.5× 48 0.5× 31 0.4× 69 1.0× 163 2.5× 23 1.2k
Lulu Cao China 19 533 1.2× 53 0.5× 107 1.3× 34 0.5× 198 3.1× 73 1.0k
Evan K. Maxwell United States 9 411 0.9× 74 0.7× 44 0.5× 24 0.4× 83 1.3× 12 948
Jemma Wu Australia 12 308 0.7× 29 0.3× 132 1.6× 42 0.6× 58 0.9× 26 626
Lindsay Ramage United Kingdom 9 764 1.7× 74 0.7× 35 0.4× 20 0.3× 91 1.4× 9 1.1k
Rui Hua China 8 377 0.8× 54 0.5× 34 0.4× 11 0.2× 90 1.4× 19 655
Bijay Jassal United Kingdom 9 1.2k 2.7× 39 0.4× 81 1.0× 43 0.7× 136 2.1× 20 1.5k

Countries citing papers authored by Peipei Ping

Since Specialization
Citations

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

Fields of papers citing papers by Peipei Ping

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peipei Ping

This figure shows the co-authorship network connecting the top 25 collaborators of Peipei Ping. A scholar is included among the top collaborators of Peipei Ping 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 Peipei Ping. Peipei Ping 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.
Bui, Alex, et al.. (2025). Explainable Biomedical Hypothesis Generation via Retrieval Augmented Generation enabled Large Language Models. SHILAP Revista de lepidopterología. 12. 100436–100436.
2.
Mirza, Bilal, Dominic C. M. Ng, Wei Wang, et al.. (2024). Missing Values in Longitudinal Proteome Dynamics Studies: Making a Case for Data Multiple Imputation. Journal of Proteome Research. 23(9). 4151–4162. 2 indexed citations
3.
Jiang, Jyun‐Yu, et al.. (2023). MIND-S is a deep-learning prediction model for elucidating protein post-translational modifications in human diseases. Cell Reports Methods. 3(3). 100430–100430. 18 indexed citations
4.
Ng, Dominic C. M., et al.. (2023). Protocol for the prediction, interpretation, and mutation evaluation of post-translational modification using MIND-S. STAR Protocols. 4(4). 102682–102682. 3 indexed citations
5.
Caufield, J. Harry, et al.. (2023). A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease <em>via</em> Biomedical Reports. Journal of Visualized Experiments. 1 indexed citations
6.
Caufield, J. Harry, et al.. (2021). Cardiovascular informatics: building a bridge to data harmony. Cardiovascular Research. 118(3). 732–745. 6 indexed citations
7.
Choi, Howard, Deborah M. Simpson, Ding Wang, et al.. (2020). Heterogeneity of proteome dynamics between connective tissue phases of adult tendon. eLife. 9. 27 indexed citations
8.
Chung, Neo Christopher, Howard Choi, Ding Wang, et al.. (2020). Identifying temporal molecular signatures underlying cardiovascular diseases: A data science platform. Journal of Molecular and Cellular Cardiology. 145. 54–58. 4 indexed citations
9.
Chung, Neo Christopher, Bilal Mirza, Howard Choi, et al.. (2019). Unsupervised classification of multi-omics data during cardiac remodeling using deep learning. Methods. 166. 66–73. 38 indexed citations
10.
Caufield, J. Harry, David A. Liem, Karol E. Watson, et al.. (2018). A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts. Journal of Visualized Experiments. 6 indexed citations
11.
Liem, David A., Sanjana Murali, Yu Shi, et al.. (2018). Phrase mining of textual data to analyze extracellular matrix protein patterns across cardiovascular disease. American Journal of Physiology-Heart and Circulatory Physiology. 315(4). H910–H924. 19 indexed citations
12.
Wang, Jie, Howard Choi, Neo Christopher Chung, et al.. (2018). Integrated Dissection of Cysteine Oxidative Post-translational Modification Proteome During Cardiac Hypertrophy. Journal of Proteome Research. 17(12). 4243–4257. 15 indexed citations
13.
Lee, Jessica, Bilal Mirza, Jie Wang, et al.. (2018). Antioxidant Enzymes Exhibit Resistance Against Oxidative Stress Regulation. Journal of Molecular and Cellular Cardiology. 124. 103–103. 1 indexed citations
14.
Ren, Xiang, Jiaming Shen, Meng Qu, et al.. (2017). Life-iNet: A Structured Network-Based Knowledge Exploration and Analytics System for Life Sciences. 55–60. 9 indexed citations
15.
Wang, Jie, Jessica M. Lee, David A. Liem, & Peipei Ping. (2017). HSPA5 Gene encoding Hsp70 chaperone BiP in the endoplasmic reticulum. Gene. 618. 14–23. 209 indexed citations
16.
Lam, Maggie P. Y., Peipei Ping, & Elizabeth Murphy. (2016). Proteomics Research in Cardiovascular Medicine and Biomarker Discovery. Journal of the American College of Cardiology. 68(25). 2819–2830. 65 indexed citations
17.
Lam, Maggie P. Y., Edward Lau, Dominic C. M. Ng, Ding Wang, & Peipei Ping. (2016). Cardiovascular proteomics in the era of big data: experimental and computational advances. Clinical Proteomics. 13(1). 23–23. 8 indexed citations
18.
Lau, Edward, Quan Cao, Dominic C. M. Ng, et al.. (2016). A large dataset of protein dynamics in the mammalian heart proteome. Scientific Data. 3(1). 160015–160015. 69 indexed citations
19.
Zhang, Yaoyang, Tao Xu, Bing Shan, et al.. (2015). ProteinInferencer: Confident protein identification and multiple experiment comparison for large scale proteomics projects. Journal of Proteomics. 129. 25–32. 17 indexed citations
20.
Yates, John R., Peipei Ping, & John Bergeron. (2007). The HUPO World Congress at Long Beach, 2006. Molecular & Cellular Proteomics. 6(6). 1110–1111. 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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