2017
DOI: 10.1371/journal.pone.0174696
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Mobile Genome Express (MGE): A comprehensive automatic genetic analyses pipeline with a mobile device

Abstract: The development of next-generation sequencing (NGS) technology allows to sequence whole exomes or genome. However, data analysis is still the biggest bottleneck for its wide implementation. Most laboratories still depend on manual procedures for data handling and analyses, which translates into a delay and decreased efficiency in the delivery of NGS results to doctors and patients. Thus, there is high demand for developing an automatic and an easy-to-use NGS data analyses system. We developed comprehensive, au… Show more

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Cited by 2 publications
(2 citation statements)
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“…Principal components analysis (PCA) explains the maximum variance of the data set with the least number of major components without significant loss of information [ 29 ]. The explanation of the maximum information of the samples is presented in the form of scores which were applied to detect sample patterns, groupings and similarities or differences between the substrates evaluated [ 30 ].…”
Section: Methodsmentioning
confidence: 99%
“…Principal components analysis (PCA) explains the maximum variance of the data set with the least number of major components without significant loss of information [ 29 ]. The explanation of the maximum information of the samples is presented in the form of scores which were applied to detect sample patterns, groupings and similarities or differences between the substrates evaluated [ 30 ].…”
Section: Methodsmentioning
confidence: 99%
“…Sensors can detect ammonia, methane, butane, propane, trimethylamine, methyl mercaptan, and other volatile organic components, which can be utilized for categorization and prediction analysis. To categorize data, various data processing algorithms were utilized, including principal components analysis (PCA), locally linear embedding (LLE), and linear discriminant analysis (LDA) [94][95][96]. To anticipate different marked ages of the wines, partial least squares regression (PLSR) and support vector machine (SVM) were applied as machine learning algorithms [97,98].…”
Section: Maturationmentioning
confidence: 99%