Exact Likelihood Evaluation in a Markov Mixture Model for Time Series of Seizure Counts

Biometrics ◽  
1992 ◽  
Vol 48 (1) ◽  
pp. 317 ◽  
Author(s):  
Nhu D. Le ◽  
Brian G. Leroux ◽  
Martin L. Puterman ◽  
Paul S. Albert
Keyword(s):  
Time Series ◽  
Mixture Model

scholarly journals The Autoregressive Linear Mixture Model: A Time-Series Model for an Instantaneous Mixture of Network Processes

2020 ◽  
Vol 68 ◽  
pp. 4481-4496
Author(s):  
Addison W. Bohannon ◽  
Vernon J. Lawhern ◽  
Nicholas R. Waytowich ◽  
Radu V. Balan
Keyword(s):  
Time Series ◽  
Mixture Model ◽  
Time Series Model ◽  
Linear Mixture Model

Finite mixture model: a comparison method for nonlinear time series data

2016 ◽  
Vol 7 (4) ◽  
pp. 381
Author(s):  
Rosmanjawati Binti Abdul Rahman ◽  
Seuk Wai Phoong ◽  
Mohd Tahir Ismail ◽  
Seuk Yen Phoong
Keyword(s):  
Time Series ◽  
Mixture Model ◽  
Time Series Data ◽  
Finite Mixture Model ◽  
Nonlinear Time Series ◽  
Comparison Method ◽  
Finite Mixture ◽  
Series Data

Finite mixture model: a comparison method for nonlinear time series data

2016 ◽  
Vol 7 (4) ◽  
pp. 381
Author(s):  
Seuk Yen Phoong ◽  
Mohd Tahir Ismail ◽  
Seuk Wai Phoong ◽  
Rosmanjawati Binti Abdul Rahman
Keyword(s):  
Time Series ◽  
Mixture Model ◽  
Time Series Data ◽  
Finite Mixture Model ◽  
Nonlinear Time Series ◽  
Comparison Method ◽  
Finite Mixture ◽  
Series Data

scholarly journals Time Series Mixture Model Considering Dependence to Multiple Topics

2015 ◽  
Vol 30 (2) ◽  
pp. 466-472
Author(s):  
Kentaro Sasaki ◽  
Tomohiro Yoshikawa ◽  
Takeshi Furuhashi
Keyword(s):  
Time Series ◽  
Mixture Model

scholarly journals Discovering Key Transcriptomic Regulators in Pancreatic Ductal Adenocarcinoma using Dirichlet Process Gaussian Mixture Model

2020 ◽  
Author(s):  
Sk Md Mosaddek Hossain ◽  
Aanzil Akram Halsana ◽  
Lutfunnesa Khatun ◽  
Sumanta Ray ◽  
Anirban Mukhopadhyay
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Mixture Model ◽  
Dirichlet Process ◽  
Time Series Data ◽  
Gaussian Mixture ◽  
Ductal Adenocarcinoma ◽  
Series Data ◽  
Main Text

ABSTRACTPancreatic Ductal Adenocarcinoma (PDAC) is the most lethal type of pancreatic cancer (PC), late detection of which leads to its therapeutic failure. This study aims to find out key regulatory genes and their impact on the progression of the disease helping the etiology of the disease which is still largely unknown. We leverage the landmark advantages of time-series gene expression data of this disease, and thereby the identified key regulators capture the characteristics of gene activity patterns in the progression of the cancer. We have identified the key modules and predicted gene functions of top genes from the compiled gene association network (GAN). Here, we have used the natural cubic spline regression model (splineTimeR) to identify differentially expressed genes (DEG) from the PDAC microarray time-series data downloaded from gene expression omnibus (GEO). First, we have identified key transcriptomic regulators (TR) and DNA binding transcription factors (DbTF). Subsequently, the Dirichlet process and Gaussian process (DPGP) mixture model is utilized to identify the key gene modules. A variation of the partial correlation method is utilized to analyze GAN, which is followed by a process of gene function prediction from the network. Finally, a panel of key genes related to PDAC is highlighted from each of the analyses performed.Please note: Abbreviations should be introduced at the first mention in the main text – no abbreviations lists. Suggested structure of main text (not enforced) is provided below.

A MIXTURE MODEL CLASSIFIER AND ITS APPLICATION ON THE BIOMEDICAL TIME SERIES

2012 ◽  
Vol 26 (6) ◽  
pp. 588-597
Author(s):  
Zhijun Yang ◽  
Zhengrong Yang ◽  
Trygve Eftestøl ◽  
Petter A. Steen ◽  
Weiping Lu ◽  
...  
Keyword(s):  
Time Series ◽  
Mixture Model
Sign in / Sign up

Export Citation Format

Share Document