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Predicting Breast Cancer Using an Expression Values Weighted Clinical Classifier

Predicting Breast Cancer Using an Expression Values Weighted Clinical Classifier

          
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About the Book

Clinical data, such as patient history, laboratory analysis, ultrasound parameters-which are the basis of day-to-day clinical decision support-are often used to guide the clinical management of cancer in the presence of microarray data. Several data fusion techniques are available to integrate genomics or proteomics data, but only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. To improve clinical management, these data should be fully exploited. This requires efficient algorithms to integrate these data sets and design a final classifier. LS-SVM classifiers and generalized eigenvalue/singular value decompositions are successfully used in many bioinformatics applications for prediction tasks. While bringing up the benefits of these two techniques, we propose a machine learning approach, a weighted LS-SVM classifier to integrate two data sources: microarray and clinical parameters. Thus a clinical classifier weighted with microarray data set results in significantly improved diagnosis, prognosis and prediction responses to therapy. The proposed model has been shown as a promising mathematical framework in both data fusion and non-linear classification problems. Proceeds from the sale of this book go to support an elderly disabled person.


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Product Details
  • ISBN-13: 9781515339502
  • Publisher: Createspace Independent Publishing Platform
  • Publisher Imprint: Createspace Independent Publishing Platform
  • Height: 275 mm
  • No of Pages: 40
  • Series Title: English
  • Weight: 15436 gr
  • ISBN-10: 1515339505
  • Publisher Date: 14 Dec 2018
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Spine Width: 3 mm
  • Width: 213 mm


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