Eukaryotic promoter LS-SVM with GMM kernel

GUO Shuo; YUAN Decheng; GUO Wa

doi:10.3969/j.issn.0438-1157.2013.12.058

您当前的位置：

首页 >

文章列表页 >

Eukaryotic promoter LS-SVM with GMM kernel

更新时间：2025-12-10

- Eukaryotic promoter LS-SVM with GMM kernel
- CIESC Journal Issue 12, Pages: 4662-4666(2013)
- 作者机构：
  
  1. 沈阳化工大学信息工程学院,辽宁,沈阳,100142
  2. 国网辽宁省电力有限公司铁岭供电公司人力资源部,辽宁,铁岭,112000
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0438-1157.2013.12.058
  CLC： 61
- Online First：05 December 2013，
  
  Published：05 December 2013
- 稿件说明：
移动端阅览
GUO Shuo, YUAN Decheng, GUO Wa. Eukaryotic promoter LS-SVM with GMM kernel[J]. CIESC Journal, 2013, (12): 4662-4666.
DOI：

GUO Shuo, YUAN Decheng, GUO Wa. Eukaryotic promoter LS-SVM with GMM kernel[J]. CIESC Journal, 2013, (12): 4662-4666. DOI： 10.3969/j.issn.0438-1157.2013.12.058.

摘要

由于真核启动子DNA序列结构复杂、数据量巨大，启动子序列辨识一直是一个难点。首先对真核启动子序列寡核苷酸位置分布特征进行高斯混合模型建模，能够将出现频率少但重要的基序提取出来。并将高斯混合模型作为真核启动子最小二乘支持向量机分类器中的核函数，将最小二乘支持向量机模型简化为最小二乘模型，计算量减少。辨识结果表明，该算法的辨识精度优于贝叶斯辨识算法，和RBF核LS-SVM相比，辨识精度基本相同，建模时间略有缩短。

Abstract

Recognition of gene promoter DNA sequence is difficult with the complex structure and the huge amount of data.In this paper

the positional densities of oligonucleotides are modeled by Gaussian mixture model.It can identify less frequent but important motifs

since the positional density is independent of the actual occurrence frequency of the oligonucleotide.These motifs generally correspond to the consensus sequences of transcription factor binding site.GMM is used as eukaryotic promoter LS-SVM kernel

which simplifies the LS-SVM as LS model.The algorithm is simplified and the computational complexity is decreased.The simulation results show the accuracy is improved compared with Bayesian classifier

and is same to LS-SVM with RBF kernel

moreover the model building time is shorter.

关键词

Keywords

references

Views

311

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

MWPCA blast furnace anomaly monitoring algorithm based on Gaussian mixture model

Fault detect method based on improved dynamic kernel principal component analysis

Select Filter-Wrapper characteristic variables for yield prediction of fluid catalytic cracking unit

Multiple kernel least square support vector machine model for prediction of cement clinker lime content

SVDD-based chiller sensor fault detection method and its detection efficiency

Related Author

ZHU Xiongzhuo

ZHANG Hanwen

YANG Chunjie

ZHAI Kun

DU Wenxia

LYU Feng

XIN Tao

JU Xiyuan

Related Institution

College of Control Science and Engineering, Zhejiang University

College of Physics and Information Engineering, Hebei Normal University

College of Career Technology, Hebei Normal University

State Key Laboratory of Heavy Oil Processing, China University of Petroleum-, Beijing

Institute of Information Science and Engineering, Yanshan University

⁰