Cluster analysis of gene expression dynamics

By Marco Ramoni, Paola Sebastiani, Isaac S. Kohane. Proceedings of the National Academy of Sciences of the United States of America, 2002

486 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f82b48dedf81d609c592024f4aa7ee750b3b3e5c

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In Molecular Biology

Citations in the corpus

Bayesian hierarchical clustering (2005, methodology): “Stolcke and Omohundro (1993) described an algorithm for agglomerative model merging based on marginal likelihoods in the context of hidden Markov model structure induction .”
A Quantitative Study of Gene Regulation Involved in the Immune Response of Anopheline Mosquitoes (2006, methodology): “Ramoni et al. (2002) proposed a pseudo-Bayesian autoregressive model for the time series with improper priors on the coefficients.”
Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data (2003, background): “An interesting recent proposal tries to circumvent some of HC’s limitations, by providing for a principled model-based agglomeration rule that allows for the automatic determination of the number of clusters [27].”
Information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments (2009, unknown): “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].”
Analyzing time series gene expression data (2004, background): “For example, Ramoni et al. (2002) studied clustering time series expression data based on their dynamics.”
Multiconstrained gene clustering based on generalized projections (2010, methodology): “Because the Pearson ’ s correlation coefficient is suitable for time-series gene expression data [26], we adopt it for calculating the similarity between two genes ’ log-ratio transformed profiles [8], i.e., the logarithm of the ratio between each sample point in the profile and a control…”
Clustering of time-course gene expression data using a mixed-effects model with B-splines (2003, methodology): “This is a generalization of the model used in Rice and Wu (2001) and a special case of the model by James and Suger (2002) for modelling noisy curves.”
Studying and modelling dynamic biological processes using time-series gene expression data (2012, methodology): “…that use regression analysis to group genes on the basis of their trajectories (for example, Cluster Analysis of Gene Expression Dynamics (CAGED) 43 ); methods based on graphical models (specifically, hidden Markov models (HMMs)) that group genes on the basis of their transcriptional trends,…”