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Kernel Methods and Applications in Bioinformatics

Abstract

The kernel technique is a powerful tool for constructing new pattern analysis methods. Kernel engineering provides a general approach to incorporating domain knowledge and dealing with discrete data structures. Kernel methods, especially the support vector machine (SVM), have been extensively applied in the bioinformatics field, achieving great successes. Meanwhile, the development of kernel methods has also been strongly driven by various challenging bioinformatic problems. This chapter aims to give a concise and intuitive introduction to the basic principles of the kernel technique, and demonstrate how it can be applied to solve problems with uncommon data types in bioinformatics. Section 18.1 begins with the product features to give an intuitive idea of kernel functions, then presents the definition and some properties of kernel functions, and then devotes a subsection to a brief review of kernel engineering and its applications to bioinformatics. Section 18.2 describes the standard SVM algorithm. Finally, Sect. 18.3 illustrates how kernel methods can be used to address the peptide identification and the protein homology prediction problems in bioinformatics, while Sect. 18.4 concludes.

Keywords

Support Vector Machine Kernel Function Feature Space Kernel Method String Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Abbreviations

APR

average overall ranking precision

DNA

deoxyribonucleic acid

HMM

hidden Markov model

KDD

knowledge discovery in databases

KSDP

kernel spectral dot product

PCA

principle component analysis

RBF

radial basis function

RBF

radical basis function

RKL

rank of the last relevant item

RMS

root-mean-square

RNA

ribonucleic acid

SDP

spectral dot product

SV

support vector

SVM

support vector machine

VC

Vapnik–Chervonenkis

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Copyright information

© Springer-Verlag 2014

Authors and Affiliations

  1. 1.Academy of Mathematics and Systems Science, Haidian DistrictChinese Academy of SciencesBeijingChina

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