A Coherent Pattern Mining Algorithm Based on All Contiguous Column Bicluster

Xiaohui Hu, Qiuhua Kuang, Qianhua Cai, Yun Xue, Weixing Zhou, Ying Li
{"title":"A Coherent Pattern Mining Algorithm Based on All Contiguous Column Bicluster","authors":"Xiaohui Hu, Qiuhua Kuang, Qianhua Cai, Yun Xue, Weixing Zhou, Ying Li","doi":"10.37965/jait.2022.0105","DOIUrl":null,"url":null,"abstract":"Microarray contains a large matrix of information and has been widely used by biologists and bio data scientist for monitoring combinations of genes in different organisms. The coherent patterns in all continuous columns are mined in gene microarray data matrices. It is investigated, in this study, the coherent patterns in all continuous columns in gene microarray data matrix by developing the time series similarity measure for the coherent patterns in all continuous columns, as well as the evaluation function for verifying the proposed algorithm and the corresponding biclusters. The continuous time changes are taken into account in the coherent patterns in all continuous columns, and co-expression patterns in time series are searched. In order to use all the common information between sequences, a similarity measure for the coherent patterns in continuous columns is defined in this paper. To validate the efficiency of the similarity measure to mine biological information at continuous time points, an evaluation function is defined to measure biclusters and an effective algorithm is proposed to mine the biclusters. Simulation experiments are conducted to verify the biological significance of the biclusters, which include synthetic datasets and real gene microarray datasets. The performance of the algorithm is analyzed and the results show that the algorithm is highly efficient.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"28","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"人工智能技术学报(英文)","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.37965/jait.2022.0105","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 28

Abstract

Microarray contains a large matrix of information and has been widely used by biologists and bio data scientist for monitoring combinations of genes in different organisms. The coherent patterns in all continuous columns are mined in gene microarray data matrices. It is investigated, in this study, the coherent patterns in all continuous columns in gene microarray data matrix by developing the time series similarity measure for the coherent patterns in all continuous columns, as well as the evaluation function for verifying the proposed algorithm and the corresponding biclusters. The continuous time changes are taken into account in the coherent patterns in all continuous columns, and co-expression patterns in time series are searched. In order to use all the common information between sequences, a similarity measure for the coherent patterns in continuous columns is defined in this paper. To validate the efficiency of the similarity measure to mine biological information at continuous time points, an evaluation function is defined to measure biclusters and an effective algorithm is proposed to mine the biclusters. Simulation experiments are conducted to verify the biological significance of the biclusters, which include synthetic datasets and real gene microarray datasets. The performance of the algorithm is analyzed and the results show that the algorithm is highly efficient.
一种基于全连续列双聚类的相干模式挖掘算法
微阵列包含大量信息,已被生物学家和生物数据科学家广泛用于监测不同生物体中的基因组合。在基因微阵列数据矩阵中挖掘所有连续列中的相干模式。在本研究中,通过开发所有连续列中相干模式的时间序列相似性度量,以及验证所提出算法和相应双聚类的评估函数,研究了基因微阵列数据矩阵中所有连续列的相干模式。在所有连续列中的连贯模式中考虑了连续的时间变化,并搜索了时间序列中的共表达模式。为了利用序列之间的所有公共信息,本文定义了连续列中相干模式的相似性度量。为了验证相似性度量在连续时间点挖掘生物信息的效率,定义了一个评估函数来测量双聚类,并提出了一种有效的算法来挖掘双聚类。通过模拟实验验证了双聚类的生物学意义,包括合成数据集和真实基因微阵列数据集。对该算法的性能进行了分析,结果表明该算法是高效的。
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
CiteScore
8.70
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信