首页|Peptidome data-driven comprehensive individualized monitoring of membranous nephropathy with machine learning

Peptidome data-driven comprehensive individualized monitoring of membranous nephropathy with machine learning

扫码查看
As the most common pathological type of nephrotic syndrome,membranous nephropathy(MN)presents diversity in progression trends,facing severe complications.The precise discrimination of MN from healthy people,other types of nephrotic syndrome or those with therapeutic remission has always been huge challenge in clinics,not to mention comprehensive individualized monitoring relied on minimally invasive molecular detection means.Herein,we construct a functionalized pore architecture to couple with machine learning to aid all-round peptidome enrichment and data profiling from hundreds of hu-man serum samples,and finally establish a set of defined peptide panel consisting of 12 specific feature signals.In addition to the realization of above-mentioned precise discrimination with more than 97%of sensitivity,88%of accuracy and f1 score,the simultaneously comprehensive individualized monitoring for MN can also be achieved,including conventionally screening diagnosis,congeneric distinction and prog-nostic evaluation.This work greatly advances the development of peptidome data-driven individualized monitoring means for complex diseases and undoubtedly inspire more devotion into molecular detection field.

Membranous nephropathySerum peptidomeMachine learningDisease diagnosis

Zixing Xu、Ruiying Chen、Chuanming Hao、Qionghong Xie、Chunhui Deng、Nianrong Sun

展开 >

Department of Gastroenterology and Hepatology,Zhongshan Hospital,and Department of Chemistry,Fudan University,Shanghai 200433,China

Division of Nephrology,Huashan Hospital,Fudan University,Shanghai 200040,China

School of Chemistry and Chemical Engineering,Nanchang University,Nanchang 330031,China

国家重点研发计划国家自然科学基金国家自然科学基金国家自然科学基金Shanghai Sailing Program

2018YFA050750122074019214255182200401720YF1405300

2024

中国化学快报(英文版)
中国化学会

中国化学快报(英文版)

CSTPCD
影响因子:0.771
ISSN:1001-8417
年,卷(期):2024.35(5)
  • 48