Biclustering algorithms for biological data analysis: a survey

By Sara Cordeiro Madeira, Arlindo L. Oliveira. IEEE/ACM Transactions on Computational Biology & Bioinformatics, 2004

2,222 citations recorded by the source corpus.

A large number of clustering approaches have been proposed for the analysis of gene expression data obtained from microarray experiments. However, the results from the application of standard clustering methods to genes are limited. This limitation is imposed by the existence of a number of experimental conditions where the activity of genes is uncorrelated. A similar limitation exists when clustering of conditions is performed. For this reason, a number of algorithms that perform simultaneous clustering on the row and column dimensions of the data matrix has been proposed. The goal is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this paper, we refer to this class of algorithms as biclustering. Biclustering is also referred in the literature as coclustering and direct clustering, among others names, and has also been used in fields such as information retrieval and data mining. In this comprehensive survey, we analyze a large number of existing approaches to biclustering, and classify them in accordance with the type of biclusters they can find, the patterns of biclusters that are discovered, the methods used to perform the search, the approaches used to evaluate the solution, and the target applications.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/68459ed4cff7692699d60218c1dc579cfe5e24c5

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Citations in the corpus

Advantages and limitations of current network inference methods (2010, methodology): “Modules are identified by methods that rely on clustering or biclustering”
Biclustering in data mining (2008, background): “Previously, there were published few surveys on biclustering [13,14], as well as a Wikipedia article [15].”
Multiobjective Optimization in Bioinformatics and Computational Biology (2007, background): “as biclustering algorithms, see [66]), or algorithms for”
Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling (2010, methodology): “We have also added biclustering (21) as a new clustering method.”
Survey of clustering algorithms (2005, background): “We offer more detailed discussion on how to identify appropriate number of clusters, which is particularly important in cluster validity, in the last part of the section.”
Community Detection and Mining in Social Media (2010, methodology): “There are many approaches to perform co-clustering (Madeira and Oliveira, 2004).”
The Discrete Basis Problem (2006, methodology): “The idea of coclustering was originally proposed by Hartigan in 1972 [25], but it has gained a lot of attention recently, and many new methods have been proposed; see, for example, [26], [27], and [28].”
QUBIC: a qualitative biclustering algorithm for analyses of gene expression data (2009, background): “(13), which consists of two types of biclusters, constant biclusters and coherent biclusters (23).”
Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering (2009, methodology): “SUBCLU [Kailing et al. 2004] uses the DBSCAN cluster model of densityconnected sets [Ester et al. 1996].”
Understanding Hidden Memories of Recurrent Neural Networks (2017, methodology): “Co-clustering is a widely used method for analyzing bipartite graphs, which simultaneously clusters two kinds of entities in a graph [28].”
A classification for community discovery methods in complex networks (2011, background): “In [60] there is a survey on co-clustering algorithms, while in [15] there is an interesting axiomatic framework for spatial clustering.”
Discovery of multi-dimensional modules by integrative analysis of cancer genomic data (2012, background): “A more related task “biclustering (co-clustering)” refers to a class of clustering technique s that perform simultaneous clustering of rows and columns in a data matrix (23).”
Dual Fuzzy-Possibilistic Co-clustering for Document Categorization (2007, methodology): “In the accuracy aspect, the simultaneous feature clustering performed by co-clustering can be considered as a feature selection process on the fly [3].”
Advances in Fuzzy Clustering and its Applications (2007, background): “There is a significant body of research on Coclustering or Bi-clustering using other approaches (Dhill on et al. 2003a; Madeira and Oliveira 2004 ), and it will be worthwhile to investigate specialized co-clustering approaches for obtaining a consensus of soft clusterings.”