Support Vector Machines and Their Application in Chemistry and Biotechnology: 1st Edition (Paperback) book cover

Support Vector Machines and Their Application in Chemistry and Biotechnology

1st Edition

By Yizeng Liang, Qing-Song Xu, Hong-Dong Li, Dong-Sheng Cao

CRC Press

211 pages | 70 B/W Illus.

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Support vector machines (SVMs) are used in a range of applications, including drug design, food quality control, metabolic fingerprint analysis, and microarray data-based cancer classification. While most mathematicians are well-versed in the distinctive features and empirical performance of SVMs, many chemists and biologists are not as familiar with what they are and how they work. Presenting a clear bridge between theory and application, Support Vector Machines and Their Application in Chemistry and Biotechnology provides a thorough description of the mechanism of SVMs from the point of view of chemists and biologists, enabling them to solve difficult problems with the help of these powerful tools.

Topics discussed include:

  • Background and key elements of support vector machines and applications in chemistry and biotechnology
  • Elements and algorithms of support vector classification (SVC) and support vector regression (SVR) machines, along with discussion of simulated datasets
  • The kernel function for solving nonlinear problems by using a simple linear transformation method
  • Ensemble learning of support vector machines
  • Applications of support vector machines to near-infrared data
  • Support vector machines and quantitative structure-activity/property relationship (QSAR/QSPR)
  • Quality control of traditional Chinese medicine by means of the chromatography fingerprint technique
  • The use of support vector machines in exploring the biological data produced in OMICS study

Beneficial for chemical data analysis and the modeling of complex physic-chemical and biological systems, support vector machines show promise in a myriad of areas. This book enables non-mathematicians to understand the potential of SVMs and utilize them in a host of applications.

Table of Contents

Overview of support vector machines


Maximal Interval Linear Classifier

Kernel Functions and Kernel Matrix

Optimization Theory

Elements of Support Vector Machines

Applications of Support Vector Machines

Support vector machines for classification and regression

Kernel Functions and Dimension Superiority

Notion of Kernel Functions

Kernel Matrix

Support Vector Machines for Classification

Computing SVMs for Linearly Separable Case

Computing SVMs for Linearly Inseparable Case

Application of SVC to Simulated Data

Support Vector Machines for Regression

ε-Band and ε-Insensitive Loss Function

Linear ε-SVR

Kernel-Based ε-SVR

Application of SVR to Simulated Data

Parametric Optimization for Support Vector Machines

Variable Selection for Support Vector Machines

Related Materials and Comments

VC Dimension

Kernel Functions and Quadratic Programming

Dimension Increasing versus Dimension Reducing

Appendix A: Computation of Slack Variable-Based SVMs

Appendix B: Computation of Linear ε-SVR

Kernel methods

Kernel Methods: Three Key Ingredients

Primal and Dual Forms

Nonlinear Mapping

Kernel Function and Kernel Matrix

Modularity of Kernel Methods

Kernel Principal Component Analysis

Kernel Partial Least Squares

Kernel Fisher Discriminant Analysis

Relationship between Kernel Function and SVMs

Kernel Matrix Pretreatment

Internet Resources

Ensemble learning of support vector machines

Ensemble Learning

Idea of Ensemble Learning

Diversity of Ensemble Learning

Bagging Support Vector Machines

Boosting Support Vector Machines

Boosting: A Simple Example

Boosting SVMs for Classification

Boosting SVMs for Regression

Further Consideration

Support vector machines applied to near-infrared spectroscopy

Near-Infrared Spectroscopy

Support Vector Machines for Classification of

Near-Infrared Data

Recognition of Blended Vinegar Based on

Near-Infrared Spectroscopy

Related Work on Support Vector Classification on NIR

Support Vector Machines for Quantitative Analysis of

Near-Infrared Data

Correlating Diesel Boiling Points with NIR Spectra

Using SVR

Related Work on Support Vector Regression on NIR

Some Comments

Support vector machines and QSAR/QSPR

Quantitative Structure-Activity/Property Relationship

History of QSAR/QSPR and Molecular Descriptors

Principles for QSAR Modeling

Related QSAR/QSPR Studies Using SVMs

Support Vector Machines for Regression

Dataset Description

Molecular Modeling and Descriptor Calculation

Feature Selection Using a Generalized

Cross-Validation Program

Model Internal Validation

PLS Regression Model

BPN Regression Model

SVR Model

Applicability Domain and External Validation

Model Interpretation

Support Vector Machines for Classification

Two-Step Algorithm: KPCA Plus LSVM

Dataset Description

Performance Evaluation

Effects of Model Parameters

Prediction Results for Three SAR Datasets

Support vector machines applied to traditional Chinese medicine


Traditional Chinese Medicines and Their Quality Control

Recognition of Authentic PCR and PCRV Using SVM


Data Description

Recognition of Authentic PCR and PCRV Using

Whole Chromatography Variable Selection Improves Performance of SVM

Some Remarks

Support vector machines applied to OMICS study

A Brief Description of OMICS Study

Support Vector Machines in Genomics

Support Vector Machines for Identifying Proteotypic

Peptides in Proteomics

Biomarker Discovery in Metabolomics Using Support

Vector Machines

Some Remarks


About the Authors

Yizeng Liang and Qing-Song Xu are with Central South University in Changsha, China.

Subject Categories

BISAC Subject Codes/Headings:
MEDICAL / Biotechnology
SCIENCE / Chemistry / Physical & Theoretical