Suppr超能文献

HMMBinder:基于 HMM -profile 特征的 DNA 结合蛋白预测。

HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features.

机构信息

Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh.

School of Computing, Information and Mathematical Sciences, The University of the South Pacific, Suva, Fiji.

出版信息

Biomed Res Int. 2017;2017:4590609. doi: 10.1155/2017/4590609. Epub 2017 Nov 14.

Abstract

DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM) as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.

摘要

DNA 结合蛋白在细胞内的各种过程中通常起着重要作用。在过去的十年中,已经使用了多种分类算法和特征提取技术来解决这个问题。在本文中,我们提出了一种称为 HMMBinder 的新型 DNA 结合蛋白预测方法。HMMBinder 使用从蛋白质序列的 HMM 轮廓中提取的单字母和双字母特征。据我们所知,这是首次将基于 HMM 轮廓的特征应用于 DNA 结合蛋白预测问题。我们将支持向量机 (SVM) 作为 HMMBinder 中的分类技术。我们的方法在标准基准数据集上进行了测试。实验表明,我们的方法优于文献中发现的最先进的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2e8/5706079/68c8cdc2565a/BMRI2017-4590609.001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验