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- W1966283005 endingPage "e9056" @default.
- W1966283005 startingPage "e9056" @default.
- W1966283005 abstract "Background Identification of genes with switch-like properties will facilitate discovery of regulatory mechanisms that underlie these properties, and will provide knowledge for the appropriate application of Boolean networks in gene regulatory models. As switch-like behavior is likely associated with tissue-specific expression, these gene products are expected to be plausible candidates as tissue-specific biomarkers. Methodology/Principal Findings In a systematic classification of genes and search for biomarkers, gene expression profiles (GEPs) of more than 16,000 genes from 2,145 mouse array samples were analyzed. Four distribution metrics (mean, standard deviation, kurtosis and skewness) were used to classify GEPs into four categories: predominantly-off, predominantly-on, graded (rheostatic), and switch-like genes. The arrays under study were also grouped and examined by tissue type. For example, arrays were categorized as ‘brain group’ and ‘non-brain group’; the Kolmogorov-Smirnov distance and Pearson correlation coefficient were then used to compare GEPs between brain and non-brain for each gene. We were thus able to identify tissue-specific biomarker candidate genes. Conclusions/Significance The methodology employed here may be used to facilitate disease-specific biomarker discovery." @default.
- W1966283005 created "2016-06-24" @default.
- W1966283005 creator A5007810865 @default.
- W1966283005 creator A5074729675 @default.
- W1966283005 creator A5079300033 @default.
- W1966283005 date "2010-02-04" @default.
- W1966283005 modified "2023-09-26" @default.
- W1966283005 title "Classification of Genes and Putative Biomarker Identification Using Distribution Metrics on Expression Profiles" @default.
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- W1966283005 doi "https://doi.org/10.1371/journal.pone.0009056" @default.
- W1966283005 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/2816221" @default.