{"title":"A Multi relational Framework for Knowledge Classification using Fuzzy Decision Tree in Biological System","authors":"M. Thangaraj, C. Vijayalakshmi","doi":"10.1145/2925995.2926026","DOIUrl":null,"url":null,"abstract":"Nowadays, development of existing technologies, there is a rapid growth in genomics and proteomics that have produced a large biological data. The sophisticated computational analyses needed to draw the conclusions from these data. Analyzing these biological data requires making sense of the data by inferring structure or generalizations from the data. Data mining plays an essential role in a variety of applications such as business organizations, health care and biological data analysis. The biological data analysis includes protein structure prediction, gene classification, cancer classification with the help of microarray data, clustering of gene expression data, statistical modeling of protein-protein interaction, etc. This paper presents a multi relational framework for the classification of protein/gene localization using fuzzy decision tree. The dataset has taken from KDD Cup 2001 task 2. The experimental results demonstrate the efficiency of the proposed method when compared with the existing system.","PeriodicalId":159180,"journal":{"name":"Proceedings of the The 11th International Knowledge Management in Organizations Conference on The changing face of Knowledge Management Impacting Society","volume":"87 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the The 11th International Knowledge Management in Organizations Conference on The changing face of Knowledge Management Impacting Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2925995.2926026","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
Nowadays, development of existing technologies, there is a rapid growth in genomics and proteomics that have produced a large biological data. The sophisticated computational analyses needed to draw the conclusions from these data. Analyzing these biological data requires making sense of the data by inferring structure or generalizations from the data. Data mining plays an essential role in a variety of applications such as business organizations, health care and biological data analysis. The biological data analysis includes protein structure prediction, gene classification, cancer classification with the help of microarray data, clustering of gene expression data, statistical modeling of protein-protein interaction, etc. This paper presents a multi relational framework for the classification of protein/gene localization using fuzzy decision tree. The dataset has taken from KDD Cup 2001 task 2. The experimental results demonstrate the efficiency of the proposed method when compared with the existing system.
如今,随着现有技术的发展,基因组学和蛋白质组学迅速发展,产生了大量的生物数据。从这些数据中得出结论需要复杂的计算分析。分析这些生物数据需要通过推断数据的结构或概括来理解数据。数据挖掘在商业组织、医疗保健和生物数据分析等各种应用中发挥着至关重要的作用。生物数据分析包括蛋白质结构预测、基因分类、借助微阵列数据进行癌症分类、基因表达数据聚类、蛋白-蛋白相互作用统计建模等。提出了一种基于模糊决策树的多关系蛋白/基因定位分类框架。数据集取自KDD Cup 2001任务2。实验结果证明了该方法与现有系统的有效性。