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Biclustering of expression data

Y Cheng, G M Church
2000 Proceedings. International Conference on Intelligent Systems for Molecular Biology  
This introduces "biclustering", or simultaneous clustering of both genes and conditions, to knowledge discovery from expression data.  ...  An efficient node-deletion algorithm is introduced to find submatrices in expression data that have low mean squared residue scores and it is shown to perform well in finding co-regulation patterns in  ...  Acknowledgments This research was conducted at the Lipper Center for Computational Genetics at the Harvard Medical School, while the first author was on academic leave from the University of Cincinnati  ... 
pmid:10977070 fatcat:ucylj3aicnej3eyvyxnem6ccum

Bottom-Up Biclustering of Expression Data

Kenneth Bryan, Padraig Cunningham
2006 2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology  
In a gene expression data matrix a bicluster is a sub-matrix of genes and conditions that exhibits a high correlation of expression activity across both rows and columns.  ...  These factors allow better representation of the natural state of functional modules in the cell. The mean squared residue is a popular measure of bicluster quality.  ...  The bicluster model of gene expression data In general biclustering refers to the 'simultaneous clustering' of both rows and columns of a data matrix [14] .  ... 
doi:10.1109/cibcb.2006.330995 fatcat:3nidgcvumfaqvg7uflsup3tx6u

Bayesian biclustering of gene expression data

Jiajun Gu, Jun S Liu
2008 BMC Genomics  
We thank the four referees and members of the Liu lab for many helpful comments and discussions.  ...  The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2164/ 9?issue=S1.  ...  Bayesian biclustering for yeast datasets We analyzed the same yeast expression data as in [15] using the BBC procedure.  ... 
doi:10.1186/1471-2164-9-s1-s4 pmid:18366617 pmcid:PMC2386069 fatcat:guj5k25ay5ctvb5qrdlpc5v62u

Seed-Based Biclustering of Gene Expression Data

Jiyuan An, Alan Wee-Chung Liew, Colleen C. Nelson, Gayle E. Woloschak
2012 PLoS ONE  
Microarray data are widely used to cluster genes according to their expression levels across experimental conditions.  ...  Biclustering finds gene clusters that have similar expression levels across a subset of conditions.  ...  In our algorithm, the expression data is normalized by zscore before biclustering.  ... 
doi:10.1371/journal.pone.0042431 pmid:22879981 pmcid:PMC3411756 fatcat:ue5l7oxfxnesbo424pvjnsgbuu

Rough Overlapping Biclustering of Gene Expression Data

Ruizhi Wang, Duoqian Miao, Gang Li, Hongyun Zhang
2007 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering  
A great number of biclustering algorithms have been proposed for analyzing gene expression data.  ...  We illustrate the method on yeast gene expression data. The experiments demonstrate the effectiveness of this approach.  ...  ACKNOWLEDGMENT The authors appreciate the availability of microarray data published in the web site [27].  ... 
doi:10.1109/bibe.2007.4375656 dblp:conf/bibe/WangMLZ07 fatcat:jqye6wl6rbgatjojylqivqal3e

Biclustering of expression data with evolutionary computation

F. Divina, J.S. Aguilar-Ruiz
2006 IEEE Transactions on Knowledge and Data Engineering  
In this work, we address the biclustering of gene expression data with evolutionary computation.  ...  In general, our approach, named SEBI, shows an excellent performance at finding patterns in gene expression data.  ...  In expression data analysis, the most important goal may not be finding the maximum bicluster or even finding a bicluster covering for the data matrix.  ... 
doi:10.1109/tkde.2006.74 fatcat:yjg36vwjkvewhfeqofyogmxjgq

Survey on Biclustering of Gene Expression Data [chapter]

Adelaide Valente Freitas, Wassim Ayadi, Mourad Elloumi, Joséluis Oliveira, Joséluis Oliveira, Jin-Kao Hao
2013 Biological Knowledge Discovery Handbook  
In this chapter, we make a survey on biclustering of gene expression data.  ...  The data generated from them are called gene expression data. The extraction of biological relevant knowledge from this data is not a trivial task.  ... 
doi:10.1002/9781118617151.ch25 fatcat:exhbwygodfdb5eahmdm5jm3oty

Analysis of Gene Expression Data Using Biclustering Algorithms [chapter]

Fadhl M.
2012 Functional Genomics  
The analysis of microarray data poses a large number of exploratory statistical aspects including clustering and biclustering algorithms, which help to identify similar patterns in gene expression data  ...  Clustering is an important explorative statistical analysis of gene expression data.  ... 
doi:10.5772/48150 fatcat:r5qjaow3hreuplbwm5po4cn52y

Biclustering of Linear Patterns In Gene Expression Data

Qinghui Gao, Christine Ho, Yingmin Jia, Jingyi Jessica Li, Haiyan Huang
2012 Journal of Computational Biology  
transcription and translation, comparison of developmental stages of multi-species); non-parametric regression; high-dimensional statistics POSITIONS Assistant Professor,  ...  ., next-generation RNA sequencing data); using statistics to understand scientific problems (e.g., mRNA isoform discovery and abundance estimation, cis-regulatory module identification, relationship between  ...  ., Pfeiffer, B., Weiszmann, R., MacArthur, S., Thomas, S., Stamatoyannopoulos, J., Eisen, M., et al., "DNA regions bound at low occupancy by transcription factors do not drive patterned reporter gene expression  ... 
doi:10.1089/cmb.2012.0032 pmid:22697238 pmcid:PMC3375643 fatcat:jcuf2dvsg5hutiypcuwqf4nc7i

Multi-objective evolutionary biclustering of gene expression data

Sushmita Mitra, Haider Banka
2006 Pattern Recognition  
Biclustering or simultaneous clustering of both genes and conditions have generated considerable interest over the past few decades, particularly related to the analysis of high-dimensional gene expression  ...  data in information retrieval, knowledge discovery, and data mining.  ...  Gene expression profile of a large bicluster on Human B-cell Lymphoma data, with 939 genes and 40 conditions.  ... 
doi:10.1016/j.patcog.2006.03.003 fatcat:v3hdunrt5jckjj2t4bv3wocmn4

Biclustering of Expression Microarray Data with Topic Models

Manuele Bicego, Pietro Lovato, Alberto Ferrarini, Massimo Delledonne
2010 2010 20th International Conference on Pattern Recognition  
This paper presents an approach to extract biclusters from expression microarray data using topic models -a class of probabilistic models which allow to detect interpretable groups of highly correlated  ...  Starting from a topic model learned from the expression matrix, some automatic rules to extract biclusters are presented, which overcome the drawbacks of previous approaches.  ...  CONCLUSIONS In this paper we presented an approach to extract biclusters from expression microarray data using PLSA.  ... 
doi:10.1109/icpr.2010.668 dblp:conf/icpr/BicegoLFD10 fatcat:dsl5rgqesbedjh4uba4jdyolfi

Biclustering of Expression Microarray Data Using Affinity Propagation [chapter]

Alessandro Farinelli, Matteo Denitto, Manuele Bicego
2011 Lecture Notes in Computer Science  
Biclustering, namely simultaneous clustering of genes and samples, represents a challenging and important research line in the expression microarray data analysis.  ...  In this paper, we investigate the use of Affinity Propagation, a popular clustering method, to perform biclustering.  ...  Algorithm 1 reports the pseudo-code of our approach: in particular the algorithm takes in input an n by m matrix that represents the expression microarray data and returns a set of biclusters.  ... 
doi:10.1007/978-3-642-24855-9_2 fatcat:v7zps4bc7vb23pmh2dbbglfhve

Review on Analysis of Gene Expression Data Using Biclustering Approaches

S. Anitha, Dr.C.P. Chandran
2016 Bonfring International Journal of Data Mining  
In this paper, survey on biclustering approaches for Gene Expression Data (GED) is carried out. Some of the issues are Correlation, Class discovery, Coherent biclusters and coregulated biclusters.  ...  Given a gene expression data matrix D=G×C= {d  ...  ) in gene expression data, avoiding the intrinsic limitations of the heuristic biclustering algorithms.  ... 
doi:10.9756/bijdm.8135 fatcat:xuv25tllmzd3zljhedg7dvrhjy

Configurable pattern-based evolutionary biclustering of gene expression data

Beatriz Pontes, Raúl Giráldez, Jesús S Aguilar-Ruiz
2013 Algorithms for Molecular Biology  
Biclustering algorithms for microarray data aim at discovering functionally related gene sets under different subsets of experimental conditions.  ...  Furthermore, our approach bases the bicluster evaluation in the use of expression patterns, being able to recognize both shifting and scaling patterns either simultaneously or not.  ...  variety of software for the analysis of gene expression data from microarrays.  ... 
doi:10.1186/1748-7188-8-4 pmid:23433178 pmcid:PMC3668234 fatcat:mxgjhqyisbby3b5usbgx5qsgaq

Analysis of biclusters with applications to gene expression data

Gahyun Park, Wojciech Szpankowski
2005 Discrete Mathematics & Theoretical Computer Science  
International audience For a given matrix of size $n \times m$ over a finite alphabet $\mathcal{A}$, a bicluster is a submatrix composed of selected columns and rows satisfying a certain property.  ...  We first consider the case where the selected biclusters are square submatrices and prove that with high probability (whp) the largest (square) bicluster having the same row-pattern is of size $\log_Q^  ...  Biclustering of gene expression data is a promising methodology for identification of groups of genes that exhibit a coherent pattern across a subset of conditions.  ... 
doi:10.46298/dmtcs.3385 fatcat:rm4nr4qvnrfxvdnuhmhvnbe6jq
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