A comprehensive sensitivity analysis of microarray breast cancer classification under feature variability
نویسندگان
چکیده
منابع مشابه
Feature Selection and Classification of MAQC-II Breast Cancer and Multiple Myeloma Microarray Gene Expression Data
Microarray data has a high dimension of variables but available datasets usually have only a small number of samples, thereby making the study of such datasets interesting and challenging. In the task of analyzing microarray data for the purpose of, e.g., predicting gene-disease association, feature selection is very important because it provides a way to handle the high dimensionality by explo...
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Feature selection technique is widely used to improve the high dimensional data analysis especially in a classification task. Cancer microarray data classification task belongs to this category. There are many researches that study the feature selection of microarray data classification. The major problem is that many feature selection methods must pre-define the number of feature. Unfortunatel...
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The DNA microarray technology enables us to measure the expression levels of thousands of genes simultaneously, providing great chance for cancer diagnosis and prognosis. The number of genes often exceeds tens of thousands, whereas the number of subjects available is often no more than a hundred. Therefore, it is necessary and important to perform gene selection for classification purpose. A go...
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MOTIVATION Methods for analyzing cancer microarray data often face two distinct challenges: the models they infer need to perform well when classifying new tissue samples while at the same time providing an insight into the patterns and gene interactions hidden in the data. State-of-the-art supervised data mining methods often cover well only one of these aspects, motivating the development of ...
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ژورنال
عنوان ژورنال: BMC Bioinformatics
سال: 2009
ISSN: 1471-2105
DOI: 10.1186/1471-2105-10-389