scholarly journals Metabomxtr: an R package for mixture-model analysis of non-targeted metabolomics data

2014 ◽  
Vol 30 (22) ◽  
pp. 3287-3288 ◽  
Author(s):  
Michael Nodzenski ◽  
Michael J. Muehlbauer ◽  
James R. Bain ◽  
Anna C. Reisetter ◽  
William L. Lowe ◽  
...  
Keyword(s):  
Mixture Model ◽  
R Package ◽  
Model Analysis ◽  
Targeted Metabolomics ◽  
Metabolomics Data

CPVA: a web-based metabolomic tool for chromatographic peak visualization and annotation

2020 ◽  
Vol 36 (12) ◽  
pp. 3913-3915
Author(s):  
Hemi Luan ◽  
Xingen Jiang ◽  
Fenfen Ji ◽  
Zhangzhang Lan ◽  
Zongwei Cai ◽  
...  
Keyword(s):  
False Positive ◽  
Supplementary Information ◽  
Liquid Chromatography Mass Spectrometry ◽  
Targeted Metabolomics ◽  
Metabolomics Data ◽  
Web Based ◽  
Tremendous Amount ◽  
Chromatographic Peaks ◽  
User Friendly

Abstract Motivation Liquid chromatography–mass spectrometry-based non-targeted metabolomics is routinely performed to qualitatively and quantitatively analyze a tremendous amount of metabolite signals in complex biological samples. However, false-positive peaks in the datasets are commonly detected as metabolite signals by using many popular software, resulting in non-reliable measurement. Results To reduce false-positive calling, we developed an interactive web tool, termed CPVA, for visualization and accurate annotation of the detected peaks in non-targeted metabolomics data. We used a chromatogram-centric strategy to unfold the characteristics of chromatographic peaks through visualization of peak morphology metrics, with additional functions to annotate adducts, isotopes and contaminants. CPVA is a free, user-friendly tool to help users to identify peak background noises and contaminants, resulting in decrease of false-positive or redundant peak calling, thereby improving the data quality of non-targeted metabolomics studies. Availability and implementation The CPVA is freely available at http://cpva.eastus.cloudapp.azure.com. Source code and installation instructions are available on GitHub: https://github.com/13479776/cpva. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Mixture model analysis of DNA microarray images

2005 ◽  
Vol 24 (7) ◽  
pp. 901-909 ◽  
Author(s):  
K. Blekas ◽  
N.P. Galatsanos ◽  
A. Likas ◽  
I.E. Lagaris
Keyword(s):  
Mixture Model ◽  
Dna Microarray ◽  
Model Analysis

Psychopathology and resilience following traumatic injury: A latent growth mixture model analysis.

2010 ◽  
Vol 55 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Terri A. deRoon-Cassini ◽  
Anthony D. Mancini ◽  
Mark D. Rusch ◽  
George A. Bonanno
Keyword(s):  
Mixture Model ◽  
Traumatic Injury ◽  
Model Analysis ◽  
Latent Growth ◽  
Growth Mixture Model ◽  
Growth Mixture ◽  
Latent Growth Mixture Model

scholarly journals Revealing Criterial Vagueness in Inconsistencies

Open Mind ◽  
2019 ◽  
Vol 3 ◽  
pp. 41-51 ◽  
Author(s):  
Steven Verheyen ◽  
Anne White ◽  
Paul Égré
Keyword(s):  
Mixture Model ◽  
Undergraduate Students ◽  
Ad Hoc ◽  
Model Analysis ◽  
Category Membership ◽  
Category Label ◽  
Probabilistic Process ◽  
Nominal Categories

Sixty undergraduate students made category membership decisions for each of 132 candidate exemplar-category name pairs (e.g., chess – Sports) in each of two separate sessions. They were frequently inconsistent from one session to the next, both for nominal categories such as Sports and Fish, and ad hoc categories such as Things You Rescue from a Burning House. A mixture model analysis revealed that several of these inconsistencies could be attributed to criterial vagueness: participants adopting different criteria for membership in the two sessions. This finding indicates that categorization is a probabilistic process, whereby the conditions for applying a category label are not invariant. Individuals have various functional meanings of nominal categories at their disposal and entertain competing goals for ad hoc categories.

scholarly journals Improved Initialization of the EM Algorithm for Mixture Model Parameter Estimation

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 373
Author(s):  
Branislav Panić ◽  
Jernej Klemenc ◽  
Marko Nagode
Keyword(s):  
Em Algorithm ◽  
Density Estimation ◽  
Mixture Model ◽  
Expectation Maximization ◽  
State Of The Art ◽  
R Package ◽  
Likelihood Estimator ◽  
Local Optima ◽  
The Em Algorithm ◽  
Initialization Algorithm

A commonly used tool for estimating the parameters of a mixture model is the Expectation–Maximization (EM) algorithm, which is an iterative procedure that can serve as a maximum-likelihood estimator. The EM algorithm has well-documented drawbacks, such as the need for good initial values and the possibility of being trapped in local optima. Nevertheless, because of its appealing properties, EM plays an important role in estimating the parameters of mixture models. To overcome these initialization problems with EM, in this paper, we propose the Rough-Enhanced-Bayes mixture estimation (REBMIX) algorithm as a more effective initialization algorithm. Three different strategies are derived for dealing with the unknown number of components in the mixture model. These strategies are thoroughly tested on artificial datasets, density–estimation datasets and image–segmentation problems and compared with state-of-the-art initialization methods for the EM. Our proposal shows promising results in terms of clustering and density-estimation performance as well as in terms of computational efficiency. All the improvements are implemented in the rebmix R package.

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