Journal ArticleDOI
Biclustering Algorithms for Biological Data Analysis: A Survey
Reads0
Chats0
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
More filters
Journal ArticleDOI
Summarizing transactional databases with overlapped hyperrectangles
TL;DR: This work forms this problem as a set covering problem using overlapped hyperrectangles, and proves that this problem and its several variations are NP-hard, and develops an approximation algorithm Hyper which can achieve a logarithmic approximation ratio in polynomial time.
Proceedings Article
Redescription mining: structure theory and algorithms
Laxmi Parida,Naren Ramakrishnan +1 more
TL;DR: A new data mining problem--redescription mining--is introduced that unifies considerations of conceptual clustering, constructive induction, and logical formula discovery and establishes the importance of redescription mining and makes the case for a thrust in this new line of research.
Journal ArticleDOI
Molecular subtyping of cancer: Current status and moving toward clinical applications
TL;DR: Five frequently applied techniques for generating molecular data, which are micro array, RNA sequencing, quantitative polymerase chain reaction, NanoString and tissue microarray, are introduced and standardized methods should be established to help identify intrinsic subgroup signatures and build robust classifiers that pave the way toward stratified treatment of cancer patients.
Journal ArticleDOI
On interestingness measures of formal concepts
TL;DR: Interestingness measures of concepts are considered and compared with respect to various aspects, such as efficiency of computation and applicability to noisy data and performing ranking correlation.
Journal ArticleDOI
A New Conceptual Clustering Framework
TL;DR: Simple, randomized algorithms are given that discover a collection of approximate conjunctive cluster descriptions in sublinear time and connections between this conceptual clustering problem and the maximum edge biclique problem are made.
References
More filters
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.