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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Proceedings ArticleDOI
Using Formal Concept Analysis for Microarray Data Comparison.
TL;DR: In this paper, the feasibility of using formal concept analysis (FCA) as a tool for microarray data analysis is investigated, and the results show the promise of FCA as an effective tool for biomedical data analysis.
Journal ArticleDOI
An interferon response gene expression signature is activated in a subset of medulloblastomas.
TL;DR: In this paper, a gene expression signature of type I interferon response in three public gene expression data sets of medulloblastomas was analyzed, showing that significant traces of differential activation of antiviral transcriptional response can be found in three independent MED-BOB patient cohorts.
Journal ArticleDOI
Mining Pre-Surgical Patterns Able to Discriminate Post-Surgical Outcomes in the Oncological Domain
TL;DR: In this paper, the authors proposed a structured view on why, when and how to apply biclustering to mine discriminative patterns of post-surgical risk with guarantees of usability.
Book ChapterDOI
Hierarchical Clustering for Finding Symmetries and Other Patterns in Massive, High Dimensional Datasets
TL;DR: By focusing on symmetries in data, a powerful means of structuring and analyzing massive, high dimensional data stores is found, and the powerfulness of hierarchical clustering in case studies in chemistry and finance is illustrated.
Journal ArticleDOI
TriClust: A Tool for Cross-Species Analysis of Gene Regulation.
Duygu Dede,Hasan Ogul +1 more
TL;DR: The experimental results indicate that TriClust can successfully identify biologically significant triclusters and promote a useful tool for cross species analysis of gene regulation from microarray expression data and the statistical results suggest that, when available, triclustering on multi‐organism data can result in better gene clusters in comparison to bic Lustering on single‐ Organism data.
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.