Pengyao Ping
School of Medicine
College of Health
Pengyao is an NHMRC grant-funded researcher and bioinformatician within A/Prof Amare’s group. He has educational background and research experience in bioinformatics, data analytics and computer science. His research interests involve integrating large-scale clinical, genetic, and proteomic datasets using advanced statistical and machine-learning techniques to uncover the biological mechanisms underlying complex diseases. By collaborating with epidemiologists and psychiatrists, his current project leverages comprehensive datasets, including sociodemographic, drug exposure, genetic, and proteomic data from large-scale biomedical databases such as the UK Biobank and the US All of Us Research Program, to investigate the genetic associations with pharmacological treatment responses.
| Date | Type | Title | Institution Name | Country | Amount |
|---|---|---|---|---|---|
| 2021 | Scholarship | Australian Government's Research Training Program (RTP) | University of Technology Sydney (UTS) | Australia | - |
| 2018 | Award | Outstanding Master Graduates | Xiangtan University (XTU) | China | - |
| 2018 | Award | Outstanding Master Graduates in Hunan | Hunan Education Department (HED) | China | - |
| 2018 | Honour | Graduate with honours, Master of Engineering in Computer Science and Technology | Xiangtan University (XTU) | China | - |
| 2017 | Award | Great Man’s Entrustment Scholarship Subsidized Outstanding Post-graduates | Xiangtan University (XTU) | China | - |
| 2015 | Scholarship | Yearly Academic Scholarship | Xiangtan University (XTU) | China | - |
| Language | Competency |
|---|---|
| Chinese (Mandarin) | Can read, write, speak, understand spoken and peer review |
| English | Can read, write, speak, understand spoken and peer review |
| Date | Institution name | Country | Title |
|---|---|---|---|
| 2021 - 2024 | University of Technology Sydney | Australia | PhD |
| 2015 - 2018 | Xiangtan University | China | Master of Engineering |
| 2010 - 2014 | Zhoukou Normal University | China | Bachelor of Science |
| Year | Citation |
|---|---|
| 2025 | Lan, T., Su, S., Ping, P., & Li, J. (2025). Novel Design for Multi-Epitope Vaccines of COVID-19 and Critical In-Silico Assessment Steps. Current Bioinformatics, 20(10), 878-889. |
| 2025 | Ping, P., Lan, T., Su, S., Liu, W., & Li, J. (2025). How error correction affects polymerase chain reaction deduplication: A survey based on unique molecular identifier datasets of short reads. Quantitative Biology, 13(3), 23 pages. |
| 2025 | Ping, P., & Li, J. (2025). Construction of edit-distance graphs for large sets of short reads through minimizer-bucketing. Bioinformatics Advances, 5(1), vbaf081. |
| 2025 | Su, S., Ni, Z., Lan, T., Ping, P., Tang, J., Yu, Z., . . . Li, J. (2025). Predicting viral host codon fitness and path shifting through tree-based learning on codon usage biases and genomic characteristics. Scientific Reports, 15(1), 12251. Scopus2 WoS2 Europe PMC1 |
| 2025 | Ping, P., Su, S., Cai, X., Lan, T., Zhang, X., Peng, H., . . . Li, J. (2025). Noise2read: Accurately Rectify Millions of Erroneous Short Reads Through Graph Learning on Edit Distances.. Genomics Proteomics Bioinformatics, qzaf120. |
| 2024 | Lan, T., Su, S., Ping, P., Hutvagner, G., Liu, T., Pan, Y., & Li, J. (2024). Generating mutants of monotone affinity towards stronger protein complexes through adversarial learning. Nature Machine Intelligence, 6(3), 315-325. Scopus6 WoS5 |
| 2023 | Cai, X., Lan, T., Ping, P., Oliver, B., & Li, J. (2023). Intra-Host Co-Existing Strains of SARS-CoV-2 Reference Genome Uncovered by Exhaustive Computational Search. Viruses, 15(5), 1065-1-1065-19. Scopus3 WoS2 |
| 2022 | Wang, L., Tan, Y., Yang, X., Kuang, L., & Ping, P. (2022). Review on predicting pairwise relationships between human microbes, drugs and diseases: from biological data to computational models. Briefings in Bioinformatics, 23(3), 25 pages. Scopus40 WoS36 Europe PMC24 |
| 2021 | Zhang, X., Ping, P., Hutvagner, G., Blumenstein, M., & Li, J. (2021). Aberration-corrected ultrafine analysis of miRNA reads at single-base resolution: a k-mer lattice approach.. Nucleic acids research, 49(18), e106. Scopus5 WoS5 Europe PMC6 |
| 2019 | Xuan, Z., Feng, X., Yu, J., Ping, P., Zhao, H., Zhu, X., & Wang, L. (2019). A Novel Method for Predicting Disease-Associated LncRNA-MiRNA Pairs Based on the Higher-Order Orthogonal Iteration. Computational and Mathematical Methods in Medicine, 2019, 13 pages. Scopus6 WoS6 Europe PMC3 |
| 2019 | Ping, P., Wang, L., Kuang, L., Ye, S., Iqbal, M. F. B., & Pei, T. (2019). A novel method for LncRNA-disease association prediction based on an lncRNA-disease association network. IEEE ACM Transactions on Computational Biology and Bioinformatics, 16(2), 688-693. Scopus81 WoS81 Europe PMC50 |
| 2018 | Yu, J., Ping, P., Wang, L., Kuang, L., Li, X., & Wu, Z. (2018). A novel probability model for lncRNA–disease association prediction based on the naïve bayesian classifier. Genes, 9(7), 21 pages. Scopus55 WoS53 Europe PMC39 |
| 2018 | Zhou, S., Xuan, Z., Wang, L., Ping, P., & Pei, T. (2018). A Novel Model for Predicting Associations between Diseases and LncRNA-miRNA Pairs Based on a Newly Constructed Bipartite Network. Computational and Mathematical Methods in Medicine, 2018, 11 pages. Scopus8 WoS8 Europe PMC3 |
| 2018 | Zhao, H., Kuang, L., Wang, L., Ping, P., Xuan, Z., Pei, T., & Wu, Z. (2018). Prediction of microRNA-disease associations based on distance correlation set. BMC Bioinformatics, 19(1), 14 pages. Scopus32 WoS29 Europe PMC20 |
| 2018 | Wang, L., Ping, P., Kuang, L., Ye, S., Lqbal, F. M. B., & Pei, T. (2018). A novel approach based on bipartite network to predict human microbe-disease associations. Current Bioinformatics, 13(2), 141-148. Scopus23 WoS25 |
| 2017 | Ping, P., Zhu, X., & Wang, L. (2017). SIMILARITIES/DISSIMILARITIES ANALYSIS of PROTEIN SEQUENCES BASED on PCA-FFT. Journal of Biological Systems, 25(1), 29-45. Scopus15 WoS12 |
| 2017 | Zhu, X., Ping, P., Qiu, Y., & Wang, L. (2017). Similarities/dissimilarities analysis of protein sequences based on the appearance model. Journal of Computational and Theoretical Nanoscience, 14(3), 1449-1460. Scopus1 |
| Year | Citation |
|---|---|
| 2017 | Qiu, Y., Wang, L., Ping, P., & Pei, T. (2017). Method for predicting hot spot residues at protein-protein interface based on the extreme learning machine. In 2017 3rd IEEE International Conference on Computer and Communications Iccc 2017 Vol. 2018-January (pp. 2689-2698). PEOPLES R CHINA, Chengdu: IEEE. DOI |
| Year | Citation |
|---|---|
| 2024 | Ping, P., Lan, T., Su, S., Liu, W., & Li, J. (2024). How Error Correction Affects PCR Deduplication: A Survey Based on UMI Datasets of Short Reads. DOI |
| 2024 | Su, S., Ni, Z., Lan, T., Ping, P., Tang, J., Yu, Z., . . . Li, J. (2024). Predicting viral host codon fitness and path shifting through tree-based learning on codon usage biases and genomic characteristics. DOI |
| 2024 | Ping, P., Su, S., Cai, X., Lan, T., Zhang, X., Peng, H., . . . Li, J. (2024). Turn ‘noise’ to signal: accurately rectify millions of erroneous short reads through graph learning on edit distances. DOI |
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