Biological Sequence Mining Using Plausible Neural Network and its Application to Exon/intron Boundaries Prediction.

Proceedings of the ... IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology : CIBCB. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2007-01-01 DOI:10.1901/jaba.2007.2007-165

Kuochen Li, Dar-Jen Chang, Eric Rouchka, Yuan Yan Chen

{"title":"Biological Sequence Mining Using Plausible Neural Network and its Application to Exon/intron Boundaries Prediction.","authors":"Kuochen Li, Dar-Jen Chang, Eric Rouchka, Yuan Yan Chen","doi":"10.1901/jaba.2007.2007-165","DOIUrl":null,"url":null,"abstract":"<p><p>Biological sequence usually contains yet to find knowledge, and mining biological sequences usually involves a huge dataset and long computation time. Common tasks for biological sequence mining are pattern discovery, classification and clustering. The newly developed model, Plausible Neural Network (PNN), provides an intuitive and unified architecture for such a large dataset analysis. This paper introduces the basic concepts of the PNN, and explains how it is applied to biological sequence mining. The specific task of biological sequence mining, exon/intron prediction, is implemented by using PNN. The experimental results show the capability of solving biological sequence mining tasks using PNN.</p>","PeriodicalId":88962,"journal":{"name":"Proceedings of the ... IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology : CIBCB. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"2007 ","pages":"165-169"},"PeriodicalIF":0.0000,"publicationDate":"2007-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2902184/pdf/nihms137486.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ... IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology : CIBCB. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1901/jaba.2007.2007-165","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

Abstract

Biological sequence usually contains yet to find knowledge, and mining biological sequences usually involves a huge dataset and long computation time. Common tasks for biological sequence mining are pattern discovery, classification and clustering. The newly developed model, Plausible Neural Network (PNN), provides an intuitive and unified architecture for such a large dataset analysis. This paper introduces the basic concepts of the PNN, and explains how it is applied to biological sequence mining. The specific task of biological sequence mining, exon/intron prediction, is implemented by using PNN. The experimental results show the capability of solving biological sequence mining tasks using PNN.

查看原文本刊更多论文

基于似然神经网络的生物序列挖掘及其在外显子/内含子边界预测中的应用

生物序列通常包含尚未发现的知识，生物序列的挖掘通常涉及庞大的数据集和较长的计算时间。生物序列挖掘的常见任务是模式发现、分类和聚类。新开发的模型，似是而非的神经网络（PNN），为如此大的数据集分析提供了一个直观和统一的架构。本文介绍了PNN的基本概念，并说明了如何将其应用于生物序列挖掘。生物序列挖掘的具体任务外显子/内含子预测是利用PNN实现的。实验结果表明，该方法能够解决生物序列挖掘问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Proceedings of the ... IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology : CIBCB. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

自引率

0.00%

发文量