ArticleAmerican journal of translational research2011
ROC-supervised principal component analysis in connection with the diagnosis of diseases.
Article in American journal of translational research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
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- Identification and Validation of Genomic Subtypes and a Prognostic Model Based on Antigen-Presenting Cells and Tumor Microenvironment Infiltration Characteristics in Hepatocellular Carcinoma.Frontiers in oncology · 2022Article
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- Identifying Otosclerosis with Aural Acoustical Tests of Absorbance, Group Delay, Acoustic Reflex Threshold, and Otoacoustic Emissions.Journal of the American Academy of Audiology · 2017Article
- Independent validation of a mathematical genomic model for survival of glioma patients.American journal of cancer research · 2016Article
- A common variant in MTHFR influences response to chemoradiotherapy and recurrence of rectal cancer.American journal of cancer research · 2015Article
- A mathematical model for short-term vs. long-term survival in patients with glioma.American journal of cancer research · 2014Article
- Inflammation and immune system activation in aging: a mathematical approach.Scientific reports · 2013Article
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- Linear Discriminant Functions in Connection with the micro-RNA Diagnosis of Colon Cancer.Cancer informatics · 2012Article
- Prognosis of treatment response (pathological complete response) in breast cancer.Biomarker insights · 2012Article
- Application of clustering analyses to the diagnosis of Huntington disease in mice and other diseases with well-defined group boundaries.Computer methods and programs in biomedicine · 2011Article
- Mathematical prognostic biomarker models for treatment response and survival in epithelial ovarian cancer.Cancer informatics · 2011Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Principal component analysis (PCA) is a data analysis method that can deal with large volumes of data. Owing to the complexity and volume of the data generated by today's advanced technologies in genomics, pro-teomics, and metabolomics, PCA has become predominant in the medical sciences. Despite its popularity, PCA leaves much to be desired in terms of accuracy and may not be suitable for certain medical applications, such as diagnostics, where accuracy is paramount. In this study, we introduced a new PCA method, one that is carefully supervised by receiver operating characteristic (ROC) curve analysis. In order to assess its performance with respect to its ability to render an accurate differential diagnosis, and to compare its performance with that of standard PCA, we studied the striatal metabolomic profile of R6/2 Huntington disease (HD) transgenic mice, as well as that of wild type (WT) mice, using high field in vivo proton nuclear magnetic resonance (NMR) spectroscopy (9.4-Tesla). We tested both the standard PCA and our ROC-supervised PCA (using in each case both the covariance and the correlation matrix), 1) with the original R6/2 HD mice and WT mice, 2) with unknown mice, whose status had been determined via genotyping, and 3) with the ability to separate the original R6/2 mice into the two age subgroups (8 and 12 wks old). Only our ROC-supervised PCA (both with the covariance and the correlation matrix) passed all tests with a total accuracy of 100%; thus, providing evidence that it may be used for diagnostic purposes.
Indexed as
Identifiers
21416060PMC3056564What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.