Journal ArticleDOI
Biclustering Algorithms for Biological Data Analysis: A Survey
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TLDR
In this comprehensive survey, a large number of existing approaches to biclustering are analyzed, and they are classified 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.Abstract:
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.read more
Citations
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Journal ArticleDOI
Pattern-driven neighborhood search for biclustering of microarray data
TL;DR: The proposed stochastic pattern-driven neighborhood search algorithm for the biclustering problem is computationally fast and can be applied to discover significant biclusters and can also be used to effectively improve the quality of existing bicluster provided by other bic Lustering methods.
Journal ArticleDOI
Biclustering of gene expression data by an extension of mixtures of factor analyzers.
TL;DR: An extension of the mixture of factor analyzers model (MFA) allowing for simultaneous clustering of genes and conditions and an alternating expectation conditional maximization (AECM) algorithm is proposed for parameter estimation.
Journal ArticleDOI
Automated quantification of DNA demethylation effects in cells via 3D mapping of nuclear signatures and population homogeneity assessment
Arkadiusz Gertych,Kolja Wawrowsky,Erik H. Lindsley,Eugene Vishnevsky,Daniel L. Farkas,Jian Tajbakhsh +5 more
TL;DR: Topologically quantified nuclear patterns of methylated cytosine and global nuclear DNA are utilized as signatures of cellular response to the treatment of cultured cells with the demethylating anti‐cancer agents: 5‐azacytidine (5‐AZA) and octreotide (OCT).
Journal ArticleDOI
Analysis of alternative signaling pathways of endoderm induction of human embryonic stem cells identifies context specific differences
Shibin Mathew,Maria Jaramillo,Xinan Zhang,Li Ang Zhang,Alejandro Soto-Gutiérrez,Ipsita Banerjee +5 more
TL;DR: Use of FGF2, W NT3A or PI3K inhibition with high activin A may serve well in definitive endoderm induction followed by WNT3A specific signaling to direct the definitiveendoderm into late endodermal lineages.
Journal ArticleDOI
Machine-part cell formation using biclustering
TL;DR: It is presented that biclustering can be successfully applied to the Part-Machine Grouping problem and empirical results are presented to demonstrate the efficiency and accuracy of the proposed technique with respect to related ones for various formations of the problem.
References
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Book
Introduction to Algorithms
TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
Journal ArticleDOI
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.
Todd R. Golub,Todd R. Golub,Donna K. Slonim,Pablo Tamayo,Christine Huard,Michelle Gaasenbeek,Jill P. Mesirov,Hilary A. Coller,Mignon L. Loh,James R. Downing,Michael A. Caligiuri,Clara D. Bloomfield,Eric S. Lander +12 more
TL;DR: A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case and suggests a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
Journal ArticleDOI
Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling
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TL;DR: It is shown that there is diversity in gene expression among the tumours of DLBCL patients, apparently reflecting the variation in tumour proliferation rate, host response and differentiation state of the tumour.
Journal ArticleDOI
Comprehensive Identification of Cell Cycle–regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization
Paul T. Spellman,Gavin Sherlock,Gavin Sherlock,Michael Q. Zhang,Vishwanath R. Iyer,Kirk R. Anders,Michael B. Eisen,Patrick O. Brown,Patrick O. Brown,David Botstein,Bruce Futcher +10 more
TL;DR: A comprehensive catalog of yeast genes whose transcript levels vary periodically within the cell cycle is created, and it is found that the mRNA levels of more than half of these 800 genes respond to one or both of these cyclins.