A novel HMM-based clustering algorithm for the analysis of gene expression time-course data

2006 ◽  
Vol 50 (9) ◽  
pp. 2472-2494 ◽  
Author(s):  
Yujing Zeng ◽  
Javier Garcia-Frias
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Clustering Algorithm ◽  
Time Course Data ◽  
Expression Time

scholarly journals Comparative analysis of clustering methods for gene expression time course data

2004 ◽  
Vol 27 (4) ◽  
pp. 623-631 ◽  
Author(s):  
Ivan G. Costa ◽  
Francisco de A. T. de Carvalho ◽  
Marcílio C. P. de Souto
Keyword(s):  
Gene Expression ◽  
Comparative Analysis ◽  
Time Course ◽  
Clustering Methods ◽  
Time Course Data ◽  
Expression Time

A Bayesian approach to virus-gene expression time course data

2005 ◽  
Author(s):  
I.-S. Chang ◽  
Chi-Chung Wen ◽  
Yuh-Jenn Wu ◽  
P.K. Gupta ◽  
Shih Sheng Jiang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bayesian Approach ◽  
Time Course ◽  
Virus Gene ◽  
Virus Gene Expression ◽  
Time Course Data ◽  
Expression Time

scholarly journals Using hidden Markov models to analyze gene expression time course data

2003 ◽  
Vol 19 (Suppl 1) ◽  
pp. i255-i263 ◽  
Author(s):  
A. Schliep ◽  
A. Schonhuth ◽  
C. Steinhoff
Keyword(s):  
Gene Expression ◽  
Hidden Markov Models ◽  
Time Course ◽  
Markov Models ◽  
Hidden Markov ◽  
Time Course Data ◽  
Expression Time

scholarly journals Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Arika Fukushima ◽  
Masahiro Sugimoto ◽  
Satoru Hiwa ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Response To Therapy ◽  
Hcv Infection ◽  
Entire Treatment Period ◽  
Time Points ◽  
Time Course Data ◽  
Conventional Methods

Abstract Background Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient’s response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary. Results We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS. Conclusions The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases.

scholarly journals Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data

2017 ◽  
Vol 14 (2) ◽  
Author(s):  
Qihua Tan ◽  
Mads Thomassen ◽  
Mark Burton ◽  
Kristian Fredløv Mose ◽  
Klaus Ejner Andersen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Correlation Coefficient ◽  
Parametric Analysis ◽  
Time Course ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Microarray Time Course ◽  
Time Course Data ◽  
Generalized Correlation ◽  
Non Parametric

AbstractModeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

scholarly journals Gene Expression Time Course in the Human Skin during Elicitation of Allergic Contact Dermatitis

2007 ◽  
Vol 127 (11) ◽  
pp. 2585-2595 ◽  
Author(s):  
Malene B. Pedersen ◽  
Lone Skov ◽  
Torkil Menné ◽  
Jeanne D. Johansen ◽  
Jørgen Olsen
Keyword(s):  
Gene Expression ◽  
Contact Dermatitis ◽  
Allergic Contact Dermatitis ◽  
Human Skin ◽  
Time Course ◽  
Allergic Contact ◽  
Expression Time

CURVE-BASED CLUSTERING OF TIME COURSE GENE EXPRESSION DATA USING SELF-ORGANIZING MAPS

2009 ◽  
Vol 07 (04) ◽  
pp. 645-661 ◽  
Author(s):  
XIN CHEN
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Regulatory Networks ◽  
Time Course ◽  
Clustering Algorithm ◽  
Expression Patterns ◽  
Self Organizing Map ◽  
Expression Data ◽  
Wide Range ◽  
Self Organizing

There is an increasing interest in clustering time course gene expression data to investigate a wide range of biological processes. However, developing a clustering algorithm ideal for time course gene express data is still challenging. As timing is an important factor in defining true clusters, a clustering algorithm shall explore expression correlations between time points in order to achieve a high clustering accuracy. Moreover, inter-cluster gene relationships are often desired in order to facilitate the computational inference of biological pathways and regulatory networks. In this paper, a new clustering algorithm called CurveSOM is developed to offer both features above. It first presents each gene by a cubic smoothing spline fitted to the time course expression profile, and then groups genes into clusters by applying a self-organizing map-based clustering on the resulting splines. CurveSOM has been tested on three well-studied yeast cell cycle datasets, and compared with four popular programs including Cluster 3.0, GENECLUSTER, MCLUST, and SSClust. The results show that CurveSOM is a very promising tool for the exploratory analysis of time course expression data, as it is not only able to group genes into clusters with high accuracy but also able to find true time-shifted correlations of expression patterns across clusters.

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