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Tumor markers are substances, usually proteins that can be found in the blood, urine, stool, tumor tissue and more recently DNA changes, which are produced by the body in response to cancer growth. Thus far, more than 20 different tumor markers have been identified where some of them are specific for a particular type of cancer, while others are associated with several cancer types. The problem of...
We identified 90 germline single nucleotide polymorphisms (SNPs) that were informative for discriminative analysis of 9 major cancers among genotyped Framingham Heart Study participants. Support vector machines resulted in the greatest classification performance, which was in the range of 70-100%. The germline SNPs identified are based on DNA from peripheral blood lymphocytes obtained during non-invasive...
The cancer classification is a major and important study field in the medical research, and DNA microarrays have been proved to provide useful and great information for cancer classification at molecular level, compared with traditional clinical or histopathological information. Bio-markers from microarray gene expression analysis have developed to a new approach for cancer classification but still...
Gene selection is an important issue for cancer classification based on gene expression profiles. Filter and wrapper approaches are used widely for gene selection, where the former is hard to measure the relationship between genes and the latter requires lots of computation. We present a novel method, called gene boosting, to select relevant gene subsets by integrating filter and wrapper approaches...
A new approach to cancer diagnosis involves the analysis of gene expression data. However, to make sense of multidimensional DNA microarray data is a challenging task. Beyond 3 dimensions, it is very difficult for humans to make intuitive interpretation of how each data point is distributed, how they are interrelated, and how they should be categorized. Equalized orthogonal map (EOM), an approach...
Recent studies on molecular level classification of tissues have produced remarkable results, and indicated that gene expression assays could significantly aid in the development of efficient cancer diagnosis and classification platforms. However, cancer classification based on the DNA array data is still a difficult problem. The main challenge is the overwhelming number of genes relative to the number...
Recently there is an increasing interest in changing the criterion of tumor classification from morphologic to molecular. In this perspective, the problem can be regarded as a classification problem in machine learning. In this study wavelet analysis is used to extract the features from high dimensional microarray profiles. To make it easier to find the significant genes, we remove the small change...
It is crucial for cancer diagnosis and treatment to accurately identify the site of origin of a tumor. With the emergence and rapid advancement of DNA microarray technologies, constructing gene expression profiles for different cancer types has already become a promising means for cancer classification. In addition to research on binary classification such as normal versus tumor samples, which attracts...
DNA micro-array analysis allows us to study the expression level of thousands of genes simultaneously on a single experiment. The problem of marker selection has been extensively studied but in this paper we also consider the quality of the selected markers. Thus, we address the problem of selecting a small subset of genes that would be adequate enough to discriminate between the two classes of interest...
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