A Gentle Introduction to Support Vector Machines in Biomedicine

Support Vector Machines (SVMs) are among the most important recent developments in pattern recognition and statistical machine learning. They have found a great range of applications in various fields including biology and medicine. However, biomedical researchers often experience difficulties grasping both the theory and applications of these important methods because of lack of technical background. The purpose of this book is to introduce SVMs and their extensions and allow biomedical researchers to understand and apply them in real-life research in a very easy manner. The book is to consist of two volumes: theory and methods (Volume 1) and case studies (Volume 2).

Sample Chapter(s)
Chapter 1: Introduction and Book Overview (62 KB)

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• Preliminaries:
  • Introduction and Book Overview
  • Methods Used in this Book
• Case Studies and Comparative Evaluation in High-Throughput Genomic Data:
  • Application and Comparison of SVMs and Other Methods for Multicategory Microarray-Based Cancer Classification
  • Comparison of SVMs and Random Forests for Microarray-Based Cancer Classification
  • Comparison of SVMs and Kernel Ridge Regression for Microarray-Based Cancer Classification (Contributed by Zhiguo Li)
  • Application and Comparison of SVMs and Other Methods for Multicategory Classification in Microbiomics (Contributed by Mikael Henaff, Kranti Konganti, Varun Narendra, Alexander V Alekseyenko)
  • Application to Assessment of Plasma Proteome Stability
• Case Studies and Comparative Evaluation in Text Data:
  • Application and Comparison of SVMs and Other Methods for Retrieving High-Quality Content-Specific Articles (Contributed by Yindalon Aphinyanaphongs)
  • Application and Comparison of SVMs and Other Methods for Identifying Unproven Cancer Treatments on the Web (Contributed by Yindalon Aphinyanaphongs)
  • Application to Predicting Future Article Citations (Contributed by Lawrence Fu)
  • Application to Classifying Instrumentality of Article Citations (Contributed by Lawrence Fu)
  • Application and Comparison of SVMs and Other Methods for Identifying Drug–Drug Interactions-Related Literature (Contributed by Stephany Duda)
• Case Studies with Clinical Data::
  • Application to Predicting Clinical Laboratory Values
  • Application to Modeling Clinical Judgment and Guideline Compliance in the Diagnosis of Melanoma (Contributed by Andrea Sboner)
• Other Comparative Evaluation Studies of Broad Applicability:
  • Using SVMs for Causal Variable Selection
  • Application and Comparison of SVM-RFE and GLL Methods