Clustering of time-course gene expression data using functional data analysis

DOI10.1016/j.compbiolchem.2007.05.006zbMath1122.92029OpenAlexW1968194079WikidataQ31118936 ScholiaQ31118936MaRDI QIDQ2459120

Sang Hoon Kang, Joon Jin Song, Jeffrey S. Morris, Ho-Jin Lee

Publication date: 5 November 2007

Published in: Computational Biology and Chemistry (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.compbiolchem.2007.05.006

zbMATH Keywords

clusteringfunctional data analysisprincipal components analysistime-course gene expression

Mathematics Subject Classification ID

Signal detection and filtering (aspects of stochastic processes) (60G35) Biochemistry, molecular biology (92C40) Genetics and epigenetics (92D10) Computational methods for problems pertaining to biology (92-08)

Related Items

Clustering longitudinal profiles using P-splines and mixed effects models applied to time-course gene expression data, Classification for high-throughput data with an optimal subset of principal components, Clustering of temporal gene expression data by regularized spline regression and an energy based similarity measure, A fusion learning method to subgroup analysis of Alzheimer's disease, Bayesian Fourier clustering of gene expression data, Estimating equation-based causality analysis with application to microarray time series data, Functional maximum-likelihood estimation of ARH(\(p\)) models, Optimal classification for time-course gene expression data using functional data analysis

Uses Software

• fda (R)

Cites Work

• Functional data analysis
• Applied functional data analysis. Methods and case studies
• Model-Based Clustering, Discriminant Analysis, and Density Estimation