Yi Yang

Dr. Yang earned his Ph.D. in Analytical Chemistry from Fudan University from September 2017 to June 2022. Since November 2022, he has been a Qiushi Scientific and Technological Innovation Scholar (PI) at the ZJU-Hangzhou Global Scientific and Technological Innovation Center. Dr. Yang currently serves as Associate Editor of Applied Biochemistry and Biotechnology (Springer) and Youth Editorial Board Member of Glycoscience & Therapy (Elsevier). His research focuses on mass spectrometry analysis, development of bioinformatics methods for omics data, and their applications, with particular emphasis on the intersection of artificial intelligence and mass spectrometry. Addressing the scientific challenges of proteomic data interpretation in complex systems, he concentrates on three key aspects: reference information generation, spectral scoring and quality control, and bioinformatics mining. Specifically, he (1) developed deep learning-based spectral library prediction models DeepDIA and DeepGlyco, providing more comprehensive reference information for data interpretation; (2) proposed GproDIA, a comprehensive quality control algorithm for glycopeptide identification, enabling reliable differentiation of glycosylation components; and (3) established an explainable bioinformatics decision-making framework SCPDA, enabling quantitative evaluation and intelligent planning of analytical workflows. These methods have been applied in metaproteomics, glycoproteomics, single-cell proteomics, and other fields, providing scientific tools for precise measurement of proteins and modifications as well as intelligent data interpretation.

Selected publications:
1. Yang Y*, Fang Q*. Prediction of glycopeptide fragment mass spectra by deep learning. Nature Communications, 2024, 15: 2448.
2. Wang J#, Huang Y#, Lu F, Xu Q, Yang Z, Jiang Y, Shi S, Pan J, Yang Y*, Fang Q*. Benchmarking informatics workflows for data-independent acquisition single-cell proteomics. Nature Communications, 2025, 16: 10276.
3. Yang Y#, Yan G, Wu M, Yang P, Cao W#*, Qiao L*. GproDIA enables data-independent acquisition glycoproteomics with comprehensive statistical control. Nature Communications, 2021, 12: 6073.
4. Yang Y#, Liu X#, Shen C, Lin Y, Yang P, Qiao L*. In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics. Nature Communications, 2020, 11: 146.
5. Yang Y#, Zhao D#, Luo J#, Lin L, Lin Y, Shan B*, Chen H*, Qiao L*. Quantitative site-specific glycoproteomics reveals glyco-signatures for breast cancer diagnosis. Analytical Chemistry, 2025, 97(1): 114–121.