基于地标mds的高效局部配体结合位点搜索

Sungchul Kim, Lee Sael, Hwanjo Yu
{"title":"基于地标mds的高效局部配体结合位点搜索","authors":"Sungchul Kim, Lee Sael, Hwanjo Yu","doi":"10.1145/2512089.2512092","DOIUrl":null,"url":null,"abstract":"In this work, we propose a new local binding site search system, called Fast Patch-Surfer, for extending previous work, Patch-Surfer. Patch-Surfer efficiently retrieves top-k similar proteins based on new representation of proteins capturing features of their local ligand-binding site and newly defined distance function. However, further speed up is needed since in practical setting of computing dissimilarity between proteins, there are possibilities for simultaneous multiple user access on the database. We address this need for further speed up in local ligand-binding site search by exploiting landmark MultiDimensional Scaling (MDS), which is an efficient version of MDS being popularly used for representing high-dimensional dataset. According to the result, using our method, the searching time is reduced up to 99%, and it retrieves almost 80% of exact top-k results.","PeriodicalId":143937,"journal":{"name":"Data and Text Mining in Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Efficient local ligand-binding site search using landmark mds\",\"authors\":\"Sungchul Kim, Lee Sael, Hwanjo Yu\",\"doi\":\"10.1145/2512089.2512092\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, we propose a new local binding site search system, called Fast Patch-Surfer, for extending previous work, Patch-Surfer. Patch-Surfer efficiently retrieves top-k similar proteins based on new representation of proteins capturing features of their local ligand-binding site and newly defined distance function. However, further speed up is needed since in practical setting of computing dissimilarity between proteins, there are possibilities for simultaneous multiple user access on the database. We address this need for further speed up in local ligand-binding site search by exploiting landmark MultiDimensional Scaling (MDS), which is an efficient version of MDS being popularly used for representing high-dimensional dataset. According to the result, using our method, the searching time is reduced up to 99%, and it retrieves almost 80% of exact top-k results.\",\"PeriodicalId\":143937,\"journal\":{\"name\":\"Data and Text Mining in Bioinformatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data and Text Mining in Bioinformatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2512089.2512092\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data and Text Mining in Bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2512089.2512092","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

在这项工作中,我们提出了一个新的局部结合位点搜索系统,称为Fast Patch-Surfer,以扩展之前的工作Patch-Surfer。Patch-Surfer基于捕获其局部配体结合位点特征的蛋白质的新表示和新定义的距离函数有效地检索top-k相似蛋白质。然而,由于在计算蛋白质之间的不相似性的实际设置中,存在同时对数据库进行多用户访问的可能性,因此需要进一步的速度。我们通过利用具有里程碑意义的多维尺度(MDS)来解决这一问题,以进一步加快局部配体结合位点的搜索速度,MDS是MDS的有效版本,广泛用于表示高维数据集。根据结果,使用我们的方法,搜索时间减少了99%,并且检索了几乎80%的精确top-k结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient local ligand-binding site search using landmark mds
In this work, we propose a new local binding site search system, called Fast Patch-Surfer, for extending previous work, Patch-Surfer. Patch-Surfer efficiently retrieves top-k similar proteins based on new representation of proteins capturing features of their local ligand-binding site and newly defined distance function. However, further speed up is needed since in practical setting of computing dissimilarity between proteins, there are possibilities for simultaneous multiple user access on the database. We address this need for further speed up in local ligand-binding site search by exploiting landmark MultiDimensional Scaling (MDS), which is an efficient version of MDS being popularly used for representing high-dimensional dataset. According to the result, using our method, the searching time is reduced up to 99%, and it retrieves almost 80% of exact top-k results.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
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