Machine-learning based strategy identifies a robust protein biomarker panel for Alzheimer’s disease in cerebrospinal fluid
This study highlights a high accuracy, robustness and compatibility model of 12-protein panel whose detection is even based on label-free, TMT and DIA mass spectrometry or ELISA technologies, implicating its potential prospect in clinical application.
Authors
- Zhenzhen Quan (ORCID: https://orcid.org/0000-0002-2557-0371)
- Yue Qiu (ORCID: https://orcid.org/0000-0002-4590-9491)
- Hong Qing (ORCID: https://orcid.org/0000-0003-2820-450X)
- Junjun Ni (ORCID: https://orcid.org/0000-0001-8486-8146)
- Simei Ji
- Dongxu Zhao
- Yan Yan (ORCID: https://orcid.org/0000-0002-4618-7654)
- Hui Li (ORCID: https://orcid.org/0000-0003-0099-9785)
- Jun Zhang (ORCID: https://orcid.org/0000-0001-6990-5324)
- Xiaosen Hou (ORCID: https://orcid.org/0000-0002-4652-602X)
- Kefu Liu (ORCID: https://orcid.org/0000-0003-1168-8712)
Institutions
- Hebei Medical University (CN)
- Beijing Institute of Technology (CN)
- Central South University (CN)
- Beijing Biocytogen (China) (CN)
- Shenzhen Technology University (CN)
- First Affiliated Hospital of Hebei Medical University (CN)
- Shenzhen MSU-BIT University
Publication Details
- Journal
- Alzheimer s Research & Therapy
- Published
- 2025-07-04
- DOI
- https://doi.org/10.1186/s13195-025-01789-5
- Citations
- 1
- Primary Topic
- Bioinformatics and Genomic Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- National Natural Science Foundation of China
- Ministry of Science and Technology of the People's Republic of China
- Beijing Institute of Technology
- Beijing Nova Program