Results 221 to 230 of about 2,557,835 (263)

Hierarchical clustering of gene expression data

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings., 2003
Rapid development of biological technologies generates a huge amount of data, which provides a processing and global view of the gene expression levels across different conditions and over multiple stages. Analyzation and interpretation of these massive data is a challenging task.
Feng Luo 0001, Kun Tang, Latifur Khan
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An incremental clustering of gene expression data

2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009
This paper presents an incremental clustering algorithm based on DGC, a density-based algorithm we developed earlier [1]. We experimented with real-life datasets and both methods perform satisfactorily. The methods have been compared with some well-known clustering algorithms and they perform well in terms of z-score cluster validity measure.
Rosy Das, Dhruba Kumar Bhattacharyya, Jugal K. Kalita   +2 more
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Outlier analysis for gene expression data

Journal of Computer Science and Technology, 2004
The rapid developments of technologies that generate arrays of gene data enable a global view of the transcription levels of hundreds of thousands of genes simultaneously. The outlier detection problem for gene data has its importance but together with the difficulty of high dimensionality.
Chao Yan 0002, Guo-Liang Chen 0001, Yi-Fei Shen   +2 more
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Classification with Gene Expression Data

2005
A survey is given of tasks related to the construction and evaluation of classifiers applied to a renal cell cancer data set. Balanced sample splitting, non-specific filtering, linear discriminant analysis, nearest-neighbor prediction, and support vector machines are all concretely illustrated using the MLInterfaces package. Evaluations based on single
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Fuzzy Classification of Gene Expression Data

2007 IEEE International Fuzzy Systems Conference, 2007
Microarray expression studies measure, through a hybridisation process, the levels of genes expressed in biological samples. Knowledge gained from these studies is deemed increasingly important due to its potential of contributing to the understanding of fundamental questions in biology and clinical medicine.
Gerald Schaefer, Tomoharu Nakashima, Yasuyuki Yokota, Hisao Ishibuchi   +3 more
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Analyzing Microarray Gene Expression Data

2004
A multi-discipline, hands-on guide to microarray analysis of biological processes Analyzing Microarray Gene Expression Data provides a comprehensive review of available methodologies for the analysis of data derived from the latest DNA microarray technologies. Designed for biostatisticians entering the field of microarray analysis as well as biologists
McLachlan, G. J., Do, K., Ambroise, C
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Dealing with gene expression missing data

IEE Proceedings - Systems Biology, 2006
Compared evaluation of different methods is presented for estimating missing values in microarray data: weighted K-nearest neighbours imputation (KNNimpute), regression-based methods such as local least squares imputation (LLSimpute) and partial least squares imputation (PLSimpute) and Bayesian principal component analysis (BPCA).
L P, Brás, J C, Menezes
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Classification of heterogeneous gene expression data

ACM SIGKDD Explorations Newsletter, 2003
Recent advanced technologies in DNA microarray analysis are intensively applied in disease classification, especially for cancer classification. Most recent proposed gene expression classifiers can successfully classify testing samples obtained from the same microarray experiment as training samples with the assumption that the symmetric errors are ...
Benny Y. M. Fung, Vincent T. Y. Ng
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Clustering Methods for Gene-Expression Data

2009
Clustering methods are used to place items in natural patterns or convenient groups. They can be used to place genes into clusters to have similar expression patterns across the tissue samples of interest. They can also be used to cluster tissues into groups on the basis of their gene profiles.
Flack, L. K., McLachlan, G. J.
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Models for microarray gene expression data

Journal of Biopharmaceutical Statistics, 2002
This paper describes a general methodology for the analysis of differential gene expression based on microarray data. First, we characterize the data by a linear statistical model that accounts for relevant sources of variation in the data and then we consider estimation of the model parameters. Because microarray studies typically involve thousands of
Mei-Ling Ting, Lee, Weining, Lu, G A, Whitmore, David, Beier   +3 more
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