Pengyao Ping

Pengyao Ping

Adelaide Medical School

Faculty of Health and Medical Sciences

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.

  • Journals
    Year Citation
    2024 Lan, T., Su, S., Ping, P., & Li, J. (2024). Novel Design for Multi-Epitope Vaccines of COVID-19 and Critical In-Silico Assessment Steps. Current Bioinformatics, 20.
    DOI
    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.
    DOI Scopus2
    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), 19 pages.
    DOI Scopus1
    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.
    DOI Scopus26 Europe PMC15
    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.
    DOI Scopus2 Europe PMC2
    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.
    DOI Scopus6 Europe PMC2
    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.
    DOI Scopus78 Europe PMC35
    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.
    DOI Scopus53 Europe PMC31
    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.
    DOI Scopus7 Europe PMC1
    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.
    DOI Scopus32 Europe PMC19
    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.
    DOI Scopus23
    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.
    DOI Scopus15
    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.
    DOI Scopus1

  • Conference Papers
    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

  • Preprint
    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
Connect With Me
External Profiles