Xinming Li on Muck Rack

Xinming Li

China
As seen in: Arthritis & Rheumatology, Nature, Wiley Online Library, PLOS ONE, Elsevier, Journal of International Money and Finance, MDPI, Royal Society of Chemistry, Canadian Journal of Infectious Diseases & Medical Microbiology, Emerald Publishing and
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Articles

A GNN-Based Framework for Assessing Flood Impacts on Highway Networks: Integrating Network Structural, Functional, and Social Features

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By Beixuan Dong, Xinming Li, Lingzi Wu
| ascelibrary.org
Get full access to this article View all available purchase options and get full access to this article. Data Availability Statement Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. Acknowledgments The authors would like to thank the Texas Department of Transportation for providing case study data.
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Insights into the activation patterns of 1,2-dithiolane unit in biofunctional molecules

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By Jintao Zhao, Huimin Liu, Tao Liu, Jun Wu, Bingbing Chang, Tao Ma, Meirong Yi, Shao-Hua Wang, Xinming Li, Baoxin Zhang, Chao Zhang, Jianguo Fang
| Nature Verified
Abstract The cyclic five-membered disulfide 1,2-dithiolane, known for its moderate reactivity and ease of preparation, holds significant promise as a recognition unit in probe design and drug development.
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DiNovo enables high-coverage and high-confidence de novo peptide sequencing via mirror proteases and deep learning - Nature Communications

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By Zixuan Cao, Xueli Peng, Di Zhang, Piyu Zhou, Ruitao Wu, Zhiyuan Cheng, Yao Zhang, Jiaxing Dai, Yanchang Li, Xinming Li, Yaoyu He, Jinghan Yang, Haipeng Wang, Ping Xu, Lijin Yao
| Nature Verified
Abstract Despite the recent advancements driven by deep learning, de novo peptide sequencing is still constrained by incomplete peptide fragmentation and insufficient protein digestion in current single protease-based proteomic experiments. Here, we present a software system, named DiNovo, for high-coverage and high-confidence de novo peptide sequencing by leveraging the complementarity of mirror proteases.
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