A Dirichlet process biterm-based mixture model for short text stream clustering

2020 ◽  
Vol 50 (5) ◽  
pp. 1609-1619 ◽  
Author(s):  
Junyang Chen ◽  
Zhiguo Gong ◽  
Weiwen Liu
Keyword(s):  
Mixture Model ◽  
Dirichlet Process ◽  
Short Text ◽  
Stream Clustering

scholarly journals Discovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using Dirichlet process Gaussian mixture model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sk Md Mosaddek Hossain ◽  
Aanzil Akram Halsana ◽  
Lutfunnesa Khatun ◽  
Sumanta Ray ◽  
Anirban Mukhopadhyay
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Mixture Model ◽  
Cancer Progression ◽  
Dirichlet Process ◽  
Network Inference ◽  
Correlation Method ◽  
Gaussian Mixture ◽  
Ductal Adenocarcinoma ◽  
Gene Regulatory Network Inference ◽  
Gene Modules

AbstractPancreatic Ductal Adenocarcinoma (PDAC) is the most lethal type of pancreatic cancer, late detection leading to its therapeutic failure. This study aims to determine the key regulatory genes and their impacts on the disease’s progression, helping the disease’s etiology, which is still mostly unknown. We leverage the landmark advantages of time-series gene expression data of this disease and thereby identified the key regulators that capture the characteristics of gene activity patterns in the cancer progression. We have identified the key gene modules and predicted the functions of top genes from a reconstructed gene association network (GAN). A variation of the partial correlation method is utilized to analyze the GAN, followed by a gene function prediction task. Moreover, we have identified regulators for each target gene by gene regulatory network inference using the dynamical GENIE3 (dynGENIE3) algorithm. The Dirichlet process Gaussian process mixture model and cubic spline regression model (splineTimeR) are employed to identify the key gene modules and differentially expressed genes, respectively. Our analysis demonstrates a panel of key regulators and gene modules that are crucial for PDAC disease progression.

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.

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