Biclustering of Expression Data

By Yizong Cheng, George M. Church. Intelligent Systems in Molecular Biology, 2000

2,450 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b1aba0032340dada7856599fd9940345216d88b8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Biclustering algorithms for biological data analysis: a survey (2004, unknown): “Cheng and Church [6] were the first to apply biclustering to gene expression data.”
Iterative signature algorithm for the analysis of large-scale gene expression data. (2002, methodology): “level of background noise. In a recent paper [23] we introduced a new method for the analysis of large-scale gene expression data that was designed to overcome the above-mentioned problems (see Refs. [21,22] for other recent approaches). A central idea of this work was to integrate prior biological ”
Discovering Local Structure in Gene Expression Data: The Order-Preserving Submatrix Problem (2002, unknown): “The (cid:142) rst to consider local patterns ( biclusters ) in gene expression data sets were Cheng and Church (2000).”
FABIA: factor analysis for bicluster acquisition (2010, methodology): “A third example are the Cheng and Church (2000) δ-biclusters having a mean squared error below δ after fitting an additive model with a constant, a row and a column effect.”
Adaptive computation and machine learning (1998, background): “Dozens of clustering algorithms exist in the literature and a number of ad hoc clustering procedures, ranging from hierarchical clustering to k-means, have been applied to DNA microarray data [160, 7, 253, 511, 484, 124, 194], without any clear emerging consensus.”
PAC-Bayesian Analysis of Co-clustering and Beyond (2010, methodology): “…2000; El-Yaniv and Souroujon, 2001; Dhillon et al., 2003; Takamura and Matsumoto, 2003), genes and experimental conditions in bioinformatics (Cheng and Church, 2000; Cho et al., 2004; Klugeret al., 2003; Cho and Dhillon, 2008), tokens and contexts in natural language processing (Freitag,…”
/spl delta/-clusters: capturing subspace correlation in a large data set (2002, methodology): “The algorithms presented in [3] identify biclusters in a data matrix in a greedy manner.”
Revealing modular organization in the yeast transcriptional network (2002, background): “But although standard clustering methods classify genes successfully when applied to relatively small data sets, their use in the analysis of large-scale expression data is limited by two well-recognized drawback”
QUBIC: a qualitative biclustering algorithm for analyses of gene expression data (2009, methodology): “We have compared our algorithm with four existing algorithms, BIMAX (13), Iterative Signature Algorithm (ISA) (20), SAMBA (14) and HCL but did not include three earlier biclustering algorithms, Cheng–Church method (CC) (8), xMotif (24) and OPSM (12), since they were shown to have rather low performa”
SMYRF: Efficient Attention using Asymmetric Clustering (2020, methodology): “For bi-clustering, iterative algorithms have been proposed [59].”
Nonsmooth nonnegative matrix factorization (nsNMF) (2006, background): “The practical advantages of such biclustering methods has been proven in fields such as gene expression data analysis [28], [29], [30], [31], [32], [33], [34], topic extraction from documents [35], [36], and biomedical applications [37], to mention only a few.”
Cluster analysis for gene expression data: a survey (2004, methodology): “A more efficient algorithm based on multiple row/column addition/deletion (the biclustering algorithm) with time-complexity OðmnÞ was also proposed in [11].”
A generalized maximum entropy approach to bregman co-clustering and matrix approximation (2004, methodology): “This example with a uniform measure ν corresponds to the setting described in [5, 4].”
Spectral biclustering of microarray data: coclustering genes and conditions. (2003, background): “Methods for simultaneous clustering of genes and conditions were more recently proposed (Cheng and Church 2000; Getz et al. 2000; Lazzeroni and Owen 2002).”
Minimum Sum-Squared Residue Co-Clustering of Gene Expression Data (2004, methodology): “Cheng and Church [3] proposed co-clustering (biclustering) of gene expression data and advocated the importance of such simultaneous clustering of genes and conditions for discovering more coherent and meaningful clusters.”
Extracting Conserved Gene Expression Motifs from Gene Expression Data (2002, unknown): “Cheng and Church adopt an approach called \biclustering" 14 .”