Classification and feature selection algorithms for multi-class CGH data
نویسندگان
چکیده
منابع مشابه
Classification and feature selection algorithms for multi-class CGH data
UNLABELLED Recurrent chromosomal alterations provide cytological and molecular positions for the diagnosis and prognosis of cancer. Comparative genomic hybridization (CGH) has been useful in understanding these alterations in cancerous cells. CGH datasets consist of samples that are represented by large dimensional arrays of intervals. Each sample consists of long runs of intervals with losses ...
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Comparative Genomic Hybridization (CGH), when combined with microarray technology, measures copy number alterations (gains or losses of DNA segments) of a large number of genomic intervals. Selecting a small number of discriminative intervals from these genomic intervals is an important problem, known as feature selection, for accurate classification of CGH data. An important aspect of CGH data...
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ژورنال
عنوان ژورنال: Bioinformatics
سال: 2008
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/btn145