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Improving Targeted Mass Spectrometry Data Analysis with Nested Active Machine Learning.
- Source :
- Advanced Intelligent Systems (2640-4567); Aug2024, Vol. 6 Issue 8, p1-11, 11p
- Publication Year :
- 2024
-
Abstract
- Targeted mass spectrometry (MS) holds promise for precise protein and proteinârepresentative peptide identification and quantification, enhancing disease diagnosis. However, its clinical application is hindered by complex data analysis and expert review requirements. It is hypothesized that machine learning (ML) models can automate data analysis to accelerate the clinical application of MS. The approach involves an MLâdriven pipeline that extracts statistical and morphological features from an MS target region and feeds these features into ML algorithms to generate and assess predictive models. The findings demonstrate ML prediction models exhibit superior performance when trained on extracted features versus raw spectra intensity data and that random forest models exhibit robust classification performance in both internal and external validation datasets. These models remain effective across varying training dataset sizes and positive sample rates and are enhanced by a nested active learning approach. This approach can thus revolutionize clinical MS applications. [ABSTRACT FROM AUTHOR]
- Subjects :
- MACHINE learning
LIQUID chromatography-mass spectrometry
Subjects
Details
- Language :
- English
- ISSN :
- 26404567
- Volume :
- 6
- Issue :
- 8
- Database :
- Complementary Index
- Journal :
- Advanced Intelligent Systems (2640-4567)
- Publication Type :
- Academic Journal
- Accession number :
- 179071590
- Full Text :
- https://doi.org/10.1002/aisy.202300773