Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/6331
Title: Mean Squared Residue Based Biclustering Algorithms for the Analysis of Gene Expression Data
Researcher: Idicula, Sumam Mary
Guide(s): Das, Shyama
Keywords: Computer Sciences
Computational Biology
Gene expression data
Data mining
Clustering
Upload Date: 10-Jan-2013
University: Cochin University of Science and Technology
Completed Date: July, 2011
Abstract: Computational Biology is the research area that contributes to the analysis of biological data through the development of algorithms which will address significant research problems. The data from molecular newlinebiology includes DNA, RNA, Protein and Gene expression data. Gene Expression Data provides the expression level of genes under different conditions. Gene expression is the process of transcribing the DNA sequences of a gene into mRNA sequences which in turn are later translated into proteins. The number of copies of mRNA produced is called the expression level of a gene. Gene expression data is organized in the form of a matrix. Rows in the matrix represent genes and columns in the matrix represent experimental conditions. Experimental conditions newlinecan be different tissue types or time points. Entries in the gene expression matrix are real values. Through the analysis of gene expression data it is possible to determine the behavioral patterns of genes such as similarity of their behavior, nature of their interaction, their respective contribution newlineto the same pathways and so on. Similar expression patterns are exhibited newlineby the genes participating in the same biological process. These patterns newlinehave immense relevance and application in bioinformatics and clinical research. These patterns are used in the medical domain for aid in more accurate diagnosis, prognosis, treatment planning, drug discovery and newlineprotein network analysis. To identify various patterns from gene expression data, data newlinemining techniques are essential. Clustering is an important data mining technique for the analysis of gene expression data. To overcome the problems associated with clustering, biclustering is introduced. Biclustering refers to simultaneous clustering of both rows and columns of a data matrix. Clustering is a global model whereas biclustering is a newlinelocal model. Discovering local expression patterns is essential for identifying many genetic pathways that are not apparent otherwise.
Pagination: 324p.
URI: http://hdl.handle.net/10603/6331
Appears in Departments:Department of Computer Science

Files in This Item:
File Description SizeFormat 
01_title.pdfAttached File18.53 MBAdobe PDFView/Open
02_certificate & declaration.pdf18.57 MBAdobe PDFView/Open
03_acknowledgements & abstracts.pdf18.59 MBAdobe PDFView/Open
04_contents.pdf18.6 MBAdobe PDFView/Open
05_list of tables figures & abbreviations.pdf18.66 MBAdobe PDFView/Open
06_chapter 1.pdf18.66 MBAdobe PDFView/Open
07_chapter 2.pdf18.88 MBAdobe PDFView/Open
08_chapter 3.pdf19.74 MBAdobe PDFView/Open
09_chapter 4.pdf18.77 MBAdobe PDFView/Open
10_chapter 5.pdf19.55 MBAdobe PDFView/Open
11_chapter 6.pdf18.95 MBAdobe PDFView/Open
12_chapter 7.pdf18.64 MBAdobe PDFView/Open
13_references.pdf18.71 MBAdobe PDFView/Open
14_list of publications & appendix.pdf22.69 MBAdobe PDFView/Open


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