scholarly journals Novel technique for preprocessing high dimensional time-course data from DNA microarray: mathematical model-based clustering

2006 ◽  
Vol 22 (7) ◽  
pp. 843-848 ◽  
Author(s):  
K. Hakamada ◽  
M. Okamoto ◽  
T. Hanai
Keyword(s):  
Mathematical Model ◽  
Dna Microarray ◽  
Time Course ◽  
High Dimensional ◽  
Model Based Clustering ◽  
Novel Technique ◽  
Model Based ◽  
Time Course Data

scholarly journals Model-based clustering of high-dimensional data streams with online mixture of probabilistic PCA

2013 ◽  
Vol 7 (3) ◽  
pp. 281-300 ◽  
Author(s):  
Anastasios Bellas ◽  
Charles Bouveyron ◽  
Marie Cottrell ◽  
Jérôme Lacaille
Keyword(s):  
Data Streams ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Model Based Clustering ◽  
Probabilistic Pca ◽  
Model Based

scholarly journals A model-based optimization framework for the inference of regulatory interactions using time-course DNA microarray expression data

2007 ◽  
Vol 8 (1) ◽  
pp. 228 ◽  
Author(s):  
Reuben Thomas ◽  
Carlos J Paredes ◽  
Sanjay Mehrotra ◽  
Vassily Hatzimanikatis ◽  
Eleftherios T Papoutsakis
Keyword(s):  
Dna Microarray ◽  
Time Course ◽  
Expression Data ◽  
Regulatory Interactions ◽  
Microarray Expression Data ◽  
Optimization Framework ◽  
Model Based ◽  
Microarray Expression

scholarly journals Smoothing ℓ 1 -penalized estimators for high-dimensional time-course data

2007 ◽  
Vol 1 (0) ◽  
pp. 597-615 ◽  
Author(s):  
Lukas Meier ◽  
Peter Bühlmann
Keyword(s):  
Time Course ◽  
High Dimensional ◽  
Time Course Data

scholarly journals Model-based Clustering using Automatic Differentiation: Confronting Misspecification and High-Dimensional Data

2019 ◽  
Author(s):  
Siva Rajesh Kasa ◽  
Vaibhav Rajan
Keyword(s):  
Automatic Differentiation ◽  
High Dimensional Data ◽  
Penalized Likelihood ◽  
Gaussian Mixture ◽  
High Dimensional ◽  
Adjusted Rand Index ◽  
Penalty Term ◽  
Model Based Clustering ◽  
Model Based ◽  
Leibler Divergence

AbstractWe study two practically important cases of model based clustering using Gaussian Mixture Models: (1) when there is misspecification and (2) on high dimensional data, in the light of recent advances in Gradient Descent (GD) based optimization using Automatic Differentiation (AD). Our simulation studies show that EM has better clustering performance, measured by Adjusted Rand Index, compared to GD in cases of misspecification, whereas on high dimensional data GD outperforms EM. We observe that both with EM and GD there are many solutions with high likelihood but poor cluster interpretation. To address this problem we design a new penalty term for the likelihood based on the Kullback Leibler divergence between pairs of fitted components. Closed form expressions for the gradients of this penalized likelihood are difficult to derive but AD can be done effortlessly, illustrating the advantage of AD-based optimization. Extensions of this penalty for high dimensional data and for model selection are discussed. Numerical experiments on synthetic and real datasets demonstrate the efficacy of clustering using the proposed penalized likelihood approach.

scholarly journals Bayesian model-based tight clustering for time course data

2009 ◽  
Vol 25 (1) ◽  
pp. 17-38 ◽  
Author(s):  
Yongsung Joo ◽  
George Casella ◽  
James Hobert
Keyword(s):  
Bayesian Model ◽  
Time Course ◽  
Model Based ◽  
Time Course Data

scholarly journals Model-based clustering of high-dimensional data: A review

2014 ◽  
Vol 71 ◽  
pp. 52-78 ◽  
Author(s):  
Charles Bouveyron ◽  
Camille Brunet-Saumard
Keyword(s):  
High Dimensional Data ◽  
High Dimensional ◽  
Model Based Clustering ◽  
Model Based

scholarly journals Pairwise Variable Selection for High-Dimensional Model-Based Clustering

Biometrics ◽  
2009 ◽  
Vol 66 (3) ◽  
pp. 793-804 ◽  
Author(s):  
Jian Guo ◽  
Elizaveta Levina ◽  
George Michailidis ◽  
Ji Zhu
Keyword(s):  
Variable Selection ◽  
High Dimensional ◽  
Dimensional Model ◽  
Model Based Clustering ◽  
Model Based ◽  
Selection For

Classification algorithm for high‐dimensional protein markers in time‐course data

2020 ◽  
Vol 39 (28) ◽  
pp. 4201-4217
Author(s):  
Gajendra K. Vishwakarma ◽  
Atanu Bhattacharjee ◽  
Souvik Banerjee ◽  
Benoit Liquet
Keyword(s):  
Time Course ◽  
Classification Algorithm ◽  
High Dimensional ◽  
Protein Markers ◽  
Time Course Data

Gaussian mixture copulas for high-dimensional clustering and dependency-based subtyping

2019 ◽  
Author(s):  
Siva Rajesh Kasa ◽  
Sakyajit Bhattacharya ◽  
Vaibhav Rajan
Keyword(s):  
Survival Rates ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Supplementary Information ◽  
High Dimensional ◽  
Clustering Methods ◽  
Mortality And Morbidity ◽  
Model Based Clustering ◽  
Cancer Data ◽  
Model Based

Abstract Motivation The identification of sub-populations of patients with similar characteristics, called patient subtyping, is important for realizing the goals of precision medicine. Accurate subtyping is crucial for tailoring therapeutic strategies that can potentially lead to reduced mortality and morbidity. Model-based clustering, such as Gaussian mixture models, provides a principled and interpretable methodology that is widely used to identify subtypes. However, they impose identical marginal distributions on each variable; such assumptions restrict their modeling flexibility and deteriorates clustering performance. Results In this paper, we use the statistical framework of copulas to decouple the modeling of marginals from the dependencies between them. Current copula-based methods cannot scale to high dimensions due to challenges in parameter inference. We develop HD-GMCM, that addresses these challenges and, to our knowledge, is the first copula-based clustering method that can fit high-dimensional data. Our experiments on real high-dimensional gene-expression and clinical datasets show that HD-GMCM outperforms state-of-the-art model-based clustering methods, by virtue of modeling non-Gaussian data and being robust to outliers through the use of Gaussian mixture copulas. We present a case study on lung cancer data from TCGA. Clusters obtained from HD-GMCM can be interpreted based on the dependencies they model, that offers a new way of characterizing subtypes. Empirically, such modeling not only uncovers latent structure that leads to better clustering but also meaningful clinical subtypes in terms of survival rates of patients. Availability and implementation An implementation of HD-GMCM in R is available at: https://bitbucket.org/cdal/hdgmcm/. Supplementary information Supplementary data are available at Bioinformatics online.

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