scholarly journals HDP-Align: Hierarchical Dirichlet Process Clustering for Multiple Peak Alignment of Liquid Chromatography Mass Spectrometry Data

2016 ◽  
Author(s):  
Joe Wandy ◽  
Rónán Daly ◽  
Simon Rogers
Keyword(s):  
Retention Time ◽  
Dirichlet Process ◽  
Hierarchical Cluster ◽  
Mass Spectrometry Data ◽  
Hierarchical Dirichlet Process ◽  
Probabilistic Measure ◽  
Metabolomic Data ◽  
Proposed Model ◽  
Peak Mass ◽  
Single Compound

AbstractMatching peak features across multiple LC-MS runs (alignment) is an integral part of all LC-MS data processing pipelines. Alignment is challenging due to variations in the retention time of peak features across runs and the large number of peak features produced by a single compound in the analyte. In this paper, we propose a Bayesian non-parametric model that aligns peaks via a hierarchical cluster model using both peak mass and retention time. Crucially, this method provides confidence values in the form of posterior probabilities allowing the user to distinguish between aligned peaksets of high and low confidence. The results from our experiments on a diverse set of proteomic, glycomic and metabolomic data show that the proposed model is able to produce alignment results competitive to other widely-used benchmark methods, while at the same time, provide a probabilistic measure of confidence in the alignment results, thus allowing the possibility to trade precision and recall.AvailabilityOur method has been implemented as a stand-alone application in Java, available for download at http://github.com/joewandy/HDP-Align.

Variable Precision Rough Set Model Based on Covering Relation and Uncertainty Measure

2013 ◽  
Vol 694-697 ◽  
pp. 2856-2859
Author(s):  
Mei Yun Wang ◽  
Chao Wang ◽  
Da Zeng Tian
Keyword(s):  
Rough Set ◽  
Uncertainty Measure ◽  
Probabilistic Measure ◽  
Covering Relation ◽  
Practical Applications ◽  
Variable Precision ◽  
Model Based ◽  
Equivalent Relation ◽  
Proposed Model ◽  
Variable Precision Rough Set

The variable precision probabilistic rough set model is based on equivalent relation and probabilistic measure. However, the requirements of equivalent relation and probabilistic measure are too strict to satisfy in some practical applications. In order to solve the above problem, a variable precision rough set model based on covering relation and uncertainty measure is proposed. Moreover, the upper and lower approximation operators of the proposed model are given, while the properties of the operators are discussed.

scholarly journals A Gibbs Sampler for a Hierarchical Dirichlet Process Mixture Model

2019 ◽  
Author(s):  
Mark Andrews
Keyword(s):  
Mixture Model ◽  
Gibbs Sampler ◽  
Dirichlet Process ◽  
Dirichlet Process Mixture ◽  
Hierarchical Dirichlet Process ◽  
Dirichlet Process Mixture Model ◽  
Multinomial Data

A Gibbs sampler for the hierarchical Dirichlet process mixture model (HDPMM) when used with multinomial data.

Topic model for graph mining based on hierarchical Dirichlet process

2019 ◽  
Vol 4 (1) ◽  
pp. 66-77 ◽  
Author(s):  
Haibin Zhang ◽  
Shang Huating ◽  
Xianyi Wu
Keyword(s):  
Dirichlet Process ◽  
Graph Mining ◽  
Topic Model ◽  
Hierarchical Dirichlet Process

Sequence Pattern Extraction by Segmenting Time Series Data Using GP-HSMM with Hierarchical Dirichlet Process

2018 ◽  
Author(s):  
Masatoshi Nagano ◽  
Tomoaki Nakamura ◽  
Takayuki Nagai ◽  
Daichi Mochihashi ◽  
Ichiro Kobayashi ◽  
...  
Keyword(s):  
Time Series ◽  
Dirichlet Process ◽  
Time Series Data ◽  
Series Data ◽  
Pattern Extraction ◽  
Hierarchical Dirichlet Process ◽  
Sequence Pattern

scholarly journals Modeling of the process of the extinguishing gas concentration changes in the protected compartment

2018 ◽  
Vol 247 ◽  
pp. 00041
Author(s):  
Przemysław Kubica ◽  
Sylwia Boroń
Keyword(s):  
Fire Safety ◽  
Retention Time ◽  
Gas Flow ◽  
Analytical Models ◽  
Correct Prediction ◽  
Atmospheric Conditions ◽  
Flow Process ◽  
Gas Concentration ◽  
Proposed Model ◽  
Concentration Changes

The article discusses the aspect of the fire safety of rooms protected by Fixed Gaseous Extinguishing System (FGE-system). On the basis of a literature study, including the analysis of design standards, it was claimed that analytical models of gas outflow from the compartment ignore some parameters that can affect the process of extinguishing gas concentration changes in time. Correct prediction of the gas flow process may affect the retention time value, which is an important determinant of the fire safety of rooms protected by FGE-system. The density of extinguishing gas was indicated as a parameter with a large potential for extending the retention time. It was noted that the density of gas depends on atmospheric conditions like temperature, pressure and humidity, which are omitted in the standard models. In the research part, the concentration distribution of nitrogen and nitrogen-argon mixtures were analyzed using three methods. Obtained experimental data were compared with analytical calculations using a standard model (model N) and a new proposed model extended by an impact of the atmospheric conditions (model PK). Model PK showed greater accuracy of determining the process of extinguishing gas concentration changes. The new proposed model might be a valuable tool for further analysis of gas flow through the room.

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