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The goal of this paper is to show the use of data mining techniques to predict the Soft Tissue Sarcoma (STS) tumor progression. STS are cancers which occur in different parts of the body such as fat, muscle and nerves. The lack of effective treatments and the difficulty in predicting treatment response make them challenging for physicians, and has likely slowed the evolution of new therapeutic agents...
DNA microarrays can monitor the expression levels of thousands of genes simultaneously, providing the opportunity for the identification of genes that are differentially expressed across different conditions. Microarray datasets are generally limited to a small number of samples with a large number of gene expressions, therefore feature selection becomes a very important aspect of the microarray classification...
Soft Tissue Sarcomas (STS) are malignant tumors which emanate from soft tissues of the body. They are challenging for physicians because of the infrequency of their occurrence and non-predictable outcomes. In this paper, we propose a novel framework to classify STS which focuses on radio logically defined sub-regions, so-called 'habitats'. The distinctive habitats are regions where tumor evolution...
Learning from imbalanced data sets presents a convoluted problem both from the modeling and cost standpoints. In particular, when a class is of great interest but occurs relatively rarely such as in cases of fraud, instances of disease, and regions of interest in large-scale simulations, there is a correspondingly high cost for the misclassification of rare events. Under such circumstances, the data...
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