ArticleComputer methods and programs in biomedicine2011
Application of clustering analyses to the diagnosis of Huntington disease in mice and other diseases with well-defined group boundaries.
Article in Computer methods and programs in biomedicine, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- The Application of Unsupervised Clustering Methods to Alzheimer's Disease.Frontiers in computational neuroscience · 2019Pooled it
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- Leveraging Computational Intelligence Techniques for Diagnosing Degenerative Nerve Diseases: A Comprehensive Review, Open Challenges, and Future Research Directions.Diagnostics (Basel, Switzerland) · 2023Review
- Unsupervised Machine Learning to Identify Separable Clinical Alzheimer's Disease Sub-Populations.Brain sciences · 2021Article
- 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
- Prognosis of treatment response (pathological complete response) in breast cancer.Biomarker insights · 2012Article
- Linear Discriminant Functions in Connection with the micro-RNA Diagnosis of Colon Cancer.Cancer informatics · 2012Article
- 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 at 1 institution in 1 country.
Funding
Abstract
Nuclear magnetic resonance (NMR) spectroscopy has emerged as a technology that can provide metabolite information within organ systems in vivo. In this study, we introduced a new method of employing a clustering algorithm to develop a diagnostic model that can differentially diagnose a single unknown subject in a disease with well-defined group boundaries. We used three tests to assess the suitability and the accuracy required for diagnostic purposes of the four clustering algorithms we investigated (K-means, Fuzzy, Hierarchical, and Medoid Partitioning). To accomplish this goal, we studied the striatal metabolomic profile of R6/2 Huntington disease (HD) transgenic mice and that of wild type (WT) mice using high field in vivo proton NMR spectroscopy (9.4T). We tested all four clustering algorithms (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 weeks old). Only our diagnostic models that employed ROC-supervised Fuzzy, unsupervised Fuzzy, and ROC-supervised K-means Clustering passed all three stringent tests with 100% accuracy, indicating that they may be used for diagnostic purposes.
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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.