The Research on Identification of Gene Splice Sites by Support Vector Machine

Hongbin Li,Guangzhong He

doi:10.4236/jbise.2016.910b007

Abstract

The recognition of splicing sites is a very important step in the eukaryotic DNA se-quence analysis. Many scholars are working hard to improve the accuracy of identifi-cation. Our team carried out research on this issue based on support vector machine, which is one famous algorithm in data mining. The training and testing data is from the HS3D dataset, and excellent accuracy rate is achieved by nucleic acid sequence orthogonal coding and RBF core function, and the cross validation experiment hints that base pattern information is mainly located within 20 nucleotides upstream and downstream splice sites.

Highlights

Genomics is a discipline focusing on biological genomes and utilization of gene
The training and testing data is from the HS3D dataset, and excellent accuracy rate is achieved by nucleic acid sequence orthogonal coding and RBF core function, and the cross validation experiment hints that base pattern information is mainly located within 20 nucleotides upstream and downstream splice sites
Support vector machine (SVM) is a machine learning method based on Vapnik statistical learning theory [9], which was developed in the 90’s of last century

Summary

Introduction

Genomics is a discipline focusing on biological genomes and utilization of gene. The recognition of splicing sites is one major research direction of genomics. The recognition of splicing sites is a very important step in the eukaryotic DNA sequence analysis. Our team carried out research on this issue based on support vector machine, which is one famous algorithm in data mining.

Results

Conclusion