CLICK: a clustering algorithm with applications to gene expression analysis.

By Roded Sharan, R. Shamir. Proceedings. International Conference on Intelligent Systems for Molecular Biology, 2000

349 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/be6dd9dc4cae762896ca540b626bed6ad2e11f7e

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Citations in the corpus

Cluster graph modification problems (2002, background): “Most of these problems were shown to be NP-complete.”
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.”
Survey of clustering algorithms (2005, background): “Several recent advances on -means and other squared-error based clustering algorithms with their applications can be found in [125], [155], [222], [223], [264], and [277].”
From patterns to pathways: gene expression data analysis comes of age (2002, background): “For example, if the ‘true’ or best clustering is known (as it might be with simulated data), a good metric might measure the fraction of co-clustered pairs from the true solution that are grouped together by the new method 40,46 .”
Discovering Local Structure in Gene Expression Data: The Order-Preserving Submatrix Problem (2002, background): “Such a pattern might arise, for example, if the experiments in T represent distinct stages in the progress of a disease or in a cellular process, and the expression levels of all genes in G vary across the stages in the same way.”
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, establishing gene clusters based on their expression (Ben-Dor et al., 1999; Sharan and Shamir, 2000; Spellman et al., 1998; Tamayo et al., 1999).”
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).”
Extracting Conserved Gene Expression Motifs from Gene Expression Data (2002, methodology): “Bayes networks 11 and graph-based clustering algorithms 12 have also been used to model and analyse gene expression data.”
Adaptive computation and machine learning (1998, background): “Many other related approaches, including vector quantization [104, 484], principal component analysis, factorial analysis, self-organizing maps, NNs, and SVMs, can be found in the references.”
A Comprehensive Survey of Clustering Algorithms (2015, methodology): “The core idea ofCLICK is to carry out theminimumweight division of the graph with iteration in order to generate the clusters.”
Novel Unsupervised Feature Filtering of Biological Data (2006, methodology): “…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. et al ., 2004, Liu H. et al ., 2002).”
CLICK and EXPANDER: a system for clustering and visualizing gene expression data (2003, methodology): “The algorithm builds on the HCS algorithm of Hartuv and Shamir (2000).”
Judging the quality of gene expression-based clustering methods using gene annotation. (2002, background): “In addition to these long-established algorithms, new ones have been developed specifically for analysis of geneexpression data (Getz et al. 2000; Lazzeroni and Owen 2000; Shamir and Sharan 2000; Ben-Hur et al. 2002; Sinkkonen and Kaski 2002).”
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.”
Relative clustering validity criteria: A comparative overview (2010, background): “Indeed, it is known that this term may dominate the other three terms (a, b, and c), thus causing the Rand index to become unable to properly distinguish between good and bad partitions [48,49].”