ArticleAnalytical chemistry2016
Power Normalization for Mass Spectrometry Data Analysis and Analytical Method Assessment.
Article in Analytical chemistry, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Expert Algorithm for Substance Identification Using Mass Spectrometry: Statistical Foundations in Unimolecular Reaction Rate Theory.Journal of the American Society for Mass Spectrometry · 2023Article
- Normalization methods for reducing interbatch effect without quality control samples in liquid chromatography-mass spectrometry-based studies.Analytical and bioanalytical chemistry · 2021Article
- What is Normalization? The Strategies Employed in Top-Down and Bottom-Up Proteome Analysis Workflows.Proteomes · 2019Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Biomarker profiling using mass spectrometry plays an essential role in biological studies and is highly dependent on the data analysis for sample classification. In this study, we introduced power nomination of the mass spectra as a method for systematically altering the weights of peaks at different intensity levels. In combination with the use of support vector machine method (SVM), the impact on the sample classification has been characterized using data in four studies previously reported, including the distinctions of anomeric configurations of sugars, types of bacteria, stages of melanoma, and the types of breast cancer. Comprehensive analysis of the data with normalization at different power normalization index (PNI) was developed and analysis tools, including error-PNI plots, reference profiles, and error source profiles, were used to assess the potential of the analytical methods as well as to find the proper approaches to classify the samples.
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Registered trials
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