Integrated Machine Learning Algorithms-Enhanced Predication for Cervical Cancer from Mass Spectrometry-Based Proteomics Data
Early diagnosis is critical for improving outcomes in cancer patients; however, the application of diagnostic markers derived from serum proteomic screening remains challenging. Artificial intelligence (AI), encompassing deep learning and machine learning (ML), has gained increasing prominence acros...
Saved in:
| Main Authors: | Da Zhang, Lihong Zhao, Bo Guo, Aihong Guo, Jiangbo Ding, Dongdong Tong, Bingju Wang, Zhangjian Zhou |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Bioengineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5354/12/3/269 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Optimizing imputation strategies for mass spectrometry-based proteomics considering intensity and missing value rates
by: Yuming Shi, et al.
Published: (2025-01-01) -
Generalized gingivitis-related salivary proteomic profile in pregnant women with obesity: insights into biological mechanisms assessed by Tandem Mass Spectrometry
by: Laura Teodoro de MARCHI, et al.
Published: (2025-05-01) -
scplainer: using linear models to understand mass spectrometry-based single-cell proteomics data
by: Christophe Vanderaa, et al.
Published: (2025-08-01) -
Identification of Women’s Knowledge and Practices about Cervical Cancer Risk Factors and Early Diagnosis Methods
by: Dilek Karaoğlan Gülevi, et al.
Published: (2025-08-01) -
Discovery of potential ovarian cancer biomarkers using low molecular weight blood plasma proteome profiling by mass spectrometry
by: V. E. Shevchenko, et al.
Published: (2014-07-01)