Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis

By Roded Sharan, Ron Shamir. Intelligent Systems in Molecular Biology, 2000

353 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1d6c59c552fe3f269c72dca31c63e7de11c7c84c

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Citations in the corpus

A Comprehensive Survey of Clustering Algorithms (2015, methodology): “Typical algorithms of this kind of clustering are CLICK [44] and MST-based clustering [45].”
Stability-Based Validation of Clustering Solutions (2004, background): “In general, there exist algorithms that cannot be easily understood as minimizers of a cost function H (e.g., CLICK by Sharan & Shamir, 2000 ).”
Discovering Local Structure in Gene Expression Data: The Order-Preserving Submatrix Problem (2002, unknown): “In Ben-Dor et al. (1999) and Sharan and Shamir (2000), more direct approaches to clustering are taken, using graph theoretic methods.”
Mining the Biomedical Literature in the Genomic Era: An Overview (2003, unknown): “For instance, much of the large-scale analysis of genomic data to date focuses on gene expression patterns, and particularly on establishing gene clusters based on their expression (Ben-Dor et al., 1999; Sharan and Shamir, 2000; Spellman et al., 1998; Tamayo et al., 1999).”
Identification of functional modules using network topology and high-throughput data (2007, methodology): “Similarity values were generated as in [27] using two Gaussian distributions - Nm with parameters μm, σm for similarity between mates and Nn with parameters μn, σn for all other pairs.”
Mining coherent dense subgraphs across massive biological networks for functional discovery (2005, background): “…biological networks, which are often biologically meaningful, e.g. a dense protein interaction subnetwork may correspond to a protein complex (Bader and Hogue, 2003; Spirin and Mirny, 2003), and a dense co-expression network may represent a tight co-expression cluster (Sharan and Shamir, 2000).”
Fuzzy C-means Method for Clustering Microarray Data (2003, methodology): “We have used the CLuster Identification via Connectivity Kernels (CLICK) algorithm (Sharan and Shamir, 2000) to have an estimation of the number of clusters.”
From patterns to pathways: gene expression data analysis comes of age (2002, background): “Some seek to optimize a measure of within-cluster similarity or separation between clusters, but avoid specifying the number of clusters ahead of time, instead specifying information-theoretic bounds on cluster membershi”
Comprehensive survey on hierarchical clustering algorithms and the recent developments (2022, methodology): “1 3 methods based on MST (Zahn 1971; Guan and Du 1998), CLICK (Sharan and Shamir 2000), Spectral clustering (von Luxburg 2007), improvements of Spectral clustering (Chen et al.”
CLICK and EXPANDER: a system for clustering and visualizing gene expression data (2003, methodology): “In the following we outline the visualization options and demonstrate them on part of the yeast cell-cycle dataset of (Spellman et al., 1998), clustered using CLICK. The original dataset contains samples from yeast cultures synchronized by four independent methods, as well as separate experiments in”
Survey of clustering algorithms (2005, methodology): “Some recent results can be accessed in [29], [247], and [291].”
Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space (2008, background): “This set-theoretic inspired score (Jaccard score) is a standard clustering performence metric (Kaplan et al., 2005; Krause et al., 2005; Sharan and Shamir, 2000).”
Cluster graph modification problems (2002, background): “in numerous fields including computational biology (cf. [ 16 ]), image processing (cf.”
Judging the quality of gene expression-based clustering methods using gene annotation. (2002, background): “expression data (Getz et al. 2000; Lazzeroni and Owen 2000; Shamir and Sharan 2000; Ben-Hur Kaski 2002).”
Novel Unsupervised Feature Filtering of Biological Data (2006, methodology): “After demonstrating the effectiveness of our methods on both small and large datasets, we choose a third dataset (Golub et al., 1999) that has served as a benchmark for several clustering algorithms ( Sharan and Shamir, 2000, Getz et al., 2000 and more) and feature selection methods (e.g., Liu B. e”
Clustering on the Unit Hypersphere using von Mises-Fisher Distributions (2005, methodology): “These internal measures have been earlier employed for evaluating clustering of genes (e.g., Sharan and Shamir, 2000).”