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Xiaofeng Liu
Researcher at Harvard University
Publications - 804
Citations - 15427
Xiaofeng Liu is an academic researcher from Harvard University. The author has contributed to research in topics: Magnetization & Ferromagnetism. The author has an hindex of 50, co-authored 687 publications receiving 11702 citations. Previous affiliations of Xiaofeng Liu include University of Illinois at Urbana–Champaign & Goddard Space Flight Center.
Papers
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Journal ArticleDOI
PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database.
Xia Wang,Yihang Shen,Shiwei Wang,Shiliang Li,Weilin Zhang,Xiaofeng Liu,Luhua Lai,Jianfeng Pei,Honglin Li +8 more
TL;DR: A new version of the PharmMapper web server is presented, of which the backend pharmacophore database is six times larger than the earlier one, and the expanded target data cover 450 indications and 4800 molecular functions compared to 110 indications and 349 molecular functions in the last update.
Journal ArticleDOI
PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach
Xiaofeng Liu,Sisheng Ouyang,Biao Yu,Yabo Liu,Kai Huang,Jiayu Gong,Siyuan Zheng,Zhihua Li,Honglin Li,Hualiang Jiang +9 more
TL;DR: Benefited from the highly efficient and robust triangle hashing mapping method, PharmMapper bears high throughput ability and only costs 1 h averagely to screen the whole PharmTargetDB, including over 7000 receptor-based pharmacophore models.
Proceedings ArticleDOI
Confidence Regularized Self-Training
TL;DR: Zhou et al. as discussed by the authors proposed a confidence regularized self-training (CRST) framework, which treats pseudo-labels as continuous latent variables jointly optimized via alternating optimization.
Posted Content
Confidence Regularized Self-Training.
TL;DR: A confidence regularized self-training (CRST) framework, formulated as regularizedSelf-training, that treats pseudo-labels as continuous latent variables jointly optimized via alternating optimization and proposes two types of confidence regularization: label regularization (LR) and modelRegularization (MR).
Journal ArticleDOI
Epidermal Microfluidic Electrochemical Detection System: Enhanced Sweat Sampling and Metabolite Detection
Aída Martín,Jayoung Kim,Jonas Kurniawan,Juliane R. Sempionatto,Jose R. Moreto,Guangda Tang,Alan Campbell,Andrew Shin,Min Yul Lee,Xiaofeng Liu,Joseph Wang +10 more
TL;DR: A flexible epidermal microfluidic detection platform fabricated through hybridization of lithographic and screen-printed technologies, for efficient and fast sweat sampling and continuous, real-time electrochemical monitoring of glucose and lactate levels is described.