Stratification of primary antiphospholipid syndrome by mechanistic immunophenotype: machine learning identifies distinct T-cell and T-bet+CD11c+ B cell-driven patient clusters
Futai Feng(Chinese Academy of Medical Sciences & Peking Union Medical College), Xicheng Zhang(Xi'an University of Architecture and Technology), J Chen(Chinese Academy of Medical Sciences & Peking Union Medical College), Yudong Liu(Chinese Academy of Medical Sciences & Peking Union Medical College), Shasha Wang(Chinese PLA General Hospital), Wu Zz(Chinese Academy of Medical Sciences & Peking Union Medical College), Honglin Xu(Chinese Academy of Medical Sciences & Peking Union Medical College), Yanling Zhao(Chinese Academy of Medical Sciences & Peking Union Medical College), Yongzhe Li(Chinese Academy of Medical Sciences & Peking Union Medical College), Yipei Jing(Children's Hospital of Chongqing Medical University)
Cited by 0
Related Papers
The molecular portraits of breast tumors are conserved across microarray platforms
|BMC Genomics|2006|1.5k
Clinical Utility of In-house Metagenomic Next-generation Sequencing for the Diagnosis of Lower Respiratory Tract Infections and Analysis of the Host Immune Response
|Clinical Infectious Diseases|2020|195
The molecular portraits of breast tumors are conserved across microarray platforms
|UNC Libraries|2020|191
Synthesis of Hemoglobin Conjugated Polymeric Micelle: A ZnPc Carrier with Oxygen Self-Compensating Ability for Photodynamic Therapy
|Biomacromolecules|2015|136
Endometrial preparation for frozen–thawed embryo transfer cycles: a systematic review and network meta-analysis
|Journal of Assisted Reproduction and Genetics|2021|109