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

DOI10.1016/J.CSDA.2005.07.007zbMath1445.62301OpenAlexW2139361837MaRDI QIDQ959329

Yujing Zeng, Javier Garcia-Frias

Publication date: 11 December 2008

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2005.07.007

zbMATH Keywords

clusteringhidden Markov modelstime-series analysisgene expression time-course datatemporal dependences

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (4)

Identifying cluster number for subspace projected functional data clustering ⋮ Noise fuzzy clustering of time series by autoregressive metric ⋮ Editorial: 2nd special issue on statistical signal extraction and filtering ⋮ Bayesian modeling of factorial time-course data with applications to a bone aging gene expression study

Cites Work

• Clustering of unevenly sampled gene expression time-series data
• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• Cluster analysis of gene expression dynamics
• On clustering validation techniques
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