Results 231 to 240 of about 2,557,835 (263)

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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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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FCM for Gene Expression Bioinformatics Data

2009
Clustering analysis of data from DNA microarray hybridization studies is essential for evaluating and identifying biologically significant co-expressed genes. The K-means algorithm is one of the most widely used clustering technique. It attempts to solve the clustering problem by assigning each gene to a single cluster.
Kumar Dhiraj, Santanu Kumar Rath, Korra Sathya Babu   +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
openaire   +1 more source

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
openaire   +2 more sources

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
openaire   +1 more source

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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Analysis of Gene‐Expression Data Using J‐Express

Current Protocols in Bioinformatics, 2008
AbstractThe J‐Express package has been designed to facilitate the analysis of microarray data with an emphasis on efficiency, usability, and comprehensibility. The J‐Express system provides a powerful and integrated platform for the analysis of microarray gene expression data.
Anne Kristin, Stavrum, Kjell, Petersen, Inge, Jonassen, Bjarte, Dysvik   +3 more
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Scaling of Gene Expression Data Allowing the Comparison of Different Gene Expression Platforms

2008
Serial analysis of gene expression (SAGE) and microarrays have found a widespread application, but much ambiguity exists regarding the amalgamation of the data resulting from these technologies. Cross-platform utilization of gene expression data from the SAGE and microarray technology could reduce the need for duplicate experiments and facilitate a ...
van Ruissen, Fred, Schaaf, Gerben J., Kool, Marcel, Baas, Frank, Ruijter, Jan M.   +4 more
openaire   +3 more sources