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Feature selection continues to grow in importance in many areas of science and engineering, as large datasets become increasingly common. In particular, bioscience and medical datasets routinely contain several thousands of features. For effective data mining in such datasets, tools are required that can reliably distinguish the most relevant features. The latter is a useful goal in itself (e.g. such...
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 role of micro array expression data in cancer diagnosis is very significant. Mining for useful information from such micro array data consisting of thousands of genes and a small number of samples is often a tough task. Colon cancer is the second most common cause of cancer mortality in Western countries. According to the WHO 2006 report colorectal cancer causes 655,000 deaths worldwide per year...
In the paper we present a brief survey of our results in processing of data from DNA microarray experiments obtained in our collaborative research with M.C. Sklodowska Centre of Oncology. Our experience therefore is strictly connected with problems resulting from cancer diagnosis and therapy but many results have more general issue. We focus our attention on three important stages of microarray data...
The problem of marker selection in DNA microarray experiment, due to the "curse of dimensionality", has been mostly addressed so far by linear approaches. Taking into account the fact that the domain of interest is a complex one, where non-linear interconnections and dependencies may also exist among the extremely large number of examined genes, we address the use of nonlinear tools to assess...
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...
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