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
Classifying human promoters by occupancy patterns identifies recurring sequence elements, combinatorial binding, and spatial interactions.
Xinyi Yang,Martin Vingron +1 more
TL;DR: A re-analysis of occupancy and sequence patterns in human promoters is undertaken, defining subgroups of promoters characterized by stereotypic patterns of transcription factor occupancy, and combinations of specific sequence patterns which are required for their binding.
Journal ArticleDOI
Fuzzy soft subspace clustering method for gene co-expression network analysis
Qiang Wang,Guoliang Chen +1 more
TL;DR: A fuzzy soft subspace clustering method for detecting overlapped clusters of locally co-expressed genes that may participate in multiple cellular processes and take on different biological functions is proposed.
Book ChapterDOI
Mean squared residue based biclustering algorithms
Stefan Gremalschi,Gulsah Altun +1 more
TL;DR: A dual biclustering algorithm which finds (k × l)-bicluster with MSR using a greedy approach and a quadratic program which reduces the size of the matrix, so that the quadRatic program can find an optimal bicluster reasonably fast.
Proceedings Article
PaCK: Scalable parameter-free clustering on K-partite graphs
Jingrui He,Hanghang Tong,Spiros Papadimitriou,Tina Eliassi-Rad,Christos Faloutsos,Jaime G. Carbonell +5 more
TL;DR: The proposed PaCK for clustering k-partite graphs can be easily generalized to the cases where certain connectivity relations are expressed as tensors, e.g., time-evolving data.
Journal ArticleDOI
Review of signal processing in genetics
TL;DR: This paper reviews applications of signal processing techniques to a number of areas of genetics that can provide biologically signi cant information to assist with sequenceanalysis.
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
Ash A. Alizadeh,Michael B. Eisen,R. Eric Davis,Izidore S. Lossos,Andreas Rosenwald,Jennifer C. Boldrick,Hajeer Sabet,Truc Tran,Xin Yu,John Powell,Liming Yang,Gerald E. Marti,Troy Moore,James I. Hudson,Li-Sheng Lu,David B. Lewis,Robert Tibshirani,Gavin Sherlock,Wing C. Chan,Timothy C. Greiner,Dennis D. Weisenburger,James O. Armitage,Roger A. Warnke,Ronald Levy,Wyndham H. Wilson,M. R. Grever,John C. Byrd,David Botstein,Patrick O. Brown,Louis M. Staudt +29 more
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.