Results 241 to 250 of about 544,008 (293)

Minimum redundancy feature selection from microarray gene expression data

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003, 2003
Selecting a small subset of genes out of the thousands of genes in microarray data is important for accurate classification of phenotypes. Widely used methods typically rank genes according to their differential expressions among phenotypes and pick the ...
C. Ding, Hanchuan Peng
semanticscholar   +1 more source

Allergen Microarrays

2005
Allergy affects more than 25% of Western populations (1) and is estimated to be the sixth leading cause of chronic disease in the United States and Western Europe. The complexity of the condition is such that hundreds of common allergens have been described, and in order to maximize diagnostic efficiency there is an urgent clinical requirement for ...
Bacarese Hamilton T.   +3 more
openaire   +5 more sources

Tissue Microarrays

2009
Modern array technologies allow for the simultaneous screening of virtually all human genes on the DNA and RNA level. Studies using such techniques have lead to the identification of hundreds of genes with a potential role in cancer or other diseases. The validation of all of these candidate genes requires in situ analysis of high numbers of clinical ...
Ana-Maria, Dancau   +3 more
openaire   +3 more sources

Deep learning approach for microarray cancer data classification

CAAI Transactions on Intelligence Technology, 2020
Analysis of microarray data is a highly challenging problem due to the inherent complexity in the nature of the data associated with higher dimensionality, smaller sample size, imbalanced number of classes, noisy data-structure, and higher variance of ...
Hema Shekar Basavegowda, G. Dagnew
semanticscholar   +1 more source

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