Results 261 to 270 of about 31,256,270 (297)

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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Quantization of Global Gene Expression Data

2006 5th International Conference on Machine Learning and Applications (ICMLA'06), 2006
Many researchers are investigating the possibility of utilizing global gene expression profile data as a platform to infer gene regulatory networks. However, heavy computational burden and measurement noises render these efforts difficult and approaches based on quantized levels are vigorously investigated as an alternative.
Tae-Hoon Chung, Marcel Brun, Seungchan Kim   +2 more
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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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Evolving clusters in gene-expression data

Information Sciences, 2006
Clustering is a useful exploratory tool for gene-expression data. Although successful applications of clustering techniques have been reported in the literature, there is no method of choice in the gene-expression analysis community. Moreover, there are only a few works that deal with the problem of automatically estimating the number of clusters in ...
Eduardo R. Hruschka, Ricardo J. G. B. Campello, Leandro Nunes de Castro   +2 more
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Classification of Gene Expression Data in an Ontology

2001
Prediction of gene function from expression profiles is an intriguing problem that has been attempted with both unsupervised clustering and supervised learning methods. By the incorporation of prior knowledge concerning gene function, supervised methods avoid some of the problems with clustering.
Herman Midelfart, Astrid Lægreid, Henryk Jan Komorowski   +2 more
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Cluster Switches in Gene Expression Data

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2018
Following the sequencing of the human genome, the next step is to understand the function of all genes in health and disease. However, experimental study of the functions of all genes in all diseases is impossible and unnecessary, as not all genes are functional in all conditions.
Maayan Hassidim, Guy Shani, Tal Shay
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Class discovery in gene expression data

Proceedings of the fifth annual international conference on Computational biology, 2001
Recent studies (Alizadeh et al, [1]; Bittner et al,[5]; Golub et al, [11]) demonstrate the discovery of putative disease subtypes from gene expression data. The underlying computational problem is to partition the set of sample tissues into statistically meaningful classes.
Amir Ben-Dor, Nir Friedman, Zohar Yakhini   +2 more
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Calibration of Microarray Gene-Expression Data

2009
Calibration of microarray measurements aims at removing systematic biases from the probe-level data to get expression estimates that linearly correlate with the transcript abundance in the studied samples. The improvement of calibration methods is an essential prerequisite for estimating absolute expression levels, which, in turn, are required for ...
Hans, Binder, Stephan, Preibisch, Hilmar, Berger   +2 more
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Normalization of Gene-Expression Microarray Data

2010
Expression microarrays are designed to quantify the amount of mRNA in a specific sample. However, this can only be done indirectly through quantifying the color intensities returned by labeled mRNA molecules bound to the array surface. Translating pixel intensities into transcript expression requires a series of computations, generically known as ...
CALZA, Stefano, Yudi Pawitan
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