Clustering of time-course gene expression data using a mixed-effects model with B-splines

By Yihui Luan, Hongzhe Li. Bioinform., 2003

405 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f5f9746576929e187ecfbeddc2fe45968440cb6a

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Citations in the corpus

A Quantitative Study of Gene Regulation Involved in the Immune Response of Anopheline Mosquitoes (2006, background): “Model-based clustering of gene expression time series data has been recently considered by, among others, Wakefield, Zhou, and Self (2003), Ramoni, Sebastiani, and Kohane (2002), Yeung, Fraley, Murua, Raftery, and Ruzzo (2001), and Luan and Li (2003).”
High-content flow cytometry and temporal data analysis for defining a cellular signature of graft-versus-host disease. (2007, background): “Recently, several algorithms treating time as a continuous variable have been introduced to deal with this issue, including spline-based methods such as linear, cubic and B-splines (21-29).”
Information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments (2009, positive): “Most of them view observed temporal gene expression profiles coming from underlying smooth curves and cluster genes based on estimated expression profiles obtained from nonparametric smoothing [2,13-25].”
Functional clustering and identifying substructures of longitudinal data (2007, methodology): “These approaches may also take advantage of model-based techniques, as in the functional clustering (FC) models with special emphasis on sparsely sampled functional data (James and Sugar, 2003), the mixed effects modelling approach using B-splines (Luan and Li, 2003) and, very recently, the non-para”
Group SCAD regression analysis for microarray time course gene expression data (2007, neutral): “Research in analysis of such microarray time course (MTC) gene expression data has focused on two areas: clustering of MTC expression data (Luan and Li, 2003; Ma et al., 2006) and identifying genes that are temporally differentially expressed (Hong and Li, 2006; Tai and Speed, 2006; Yuan and Kendzio”