Flexible, Efficient and Interactive Retrieval for Supporting In-silico Studies of Endobacteria

S. Montani, G. Leonardi, S. Ghignone, L. Lanfranco
{"title":"Flexible, Efficient and Interactive Retrieval for Supporting In-silico Studies of Endobacteria","authors":"S. Montani, G. Leonardi, S. Ghignone, L. Lanfranco","doi":"10.1109/ICTAI.2011.12","DOIUrl":null,"url":null,"abstract":"Studying the interactions between arbuscular mycorrhizal fungi (AMFs) and their symbiotic endo bacteria has potentially strong impacts on the development of new biotechnology applications. The analysis of genomic data and syntenies is a key technique for acquiring information about phylogenetic relationships and metabolic functions of such organisms. In this paper we describe a case-based retrieval tool, which supports customized comparative genomics searches, and which is part of a modular architecture meant to support in-silico genome sequence analysis, being developed within the project BIOBITS. From a methodological viewpoint, the tool takes advantage of an abstraction technique similar to Temporal Abstractions, thus allowing to neglect un-relevant details. Retrieval is made flexible by the use of such multi-level abstractions, and efficient by the use of proper taxonomical index structures. Moreover, end-users are allowed to progressively relax or refine their queries, in an interactive way. A case study taken from the application domain is used to illustrate the approach.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTAI.2011.12","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Studying the interactions between arbuscular mycorrhizal fungi (AMFs) and their symbiotic endo bacteria has potentially strong impacts on the development of new biotechnology applications. The analysis of genomic data and syntenies is a key technique for acquiring information about phylogenetic relationships and metabolic functions of such organisms. In this paper we describe a case-based retrieval tool, which supports customized comparative genomics searches, and which is part of a modular architecture meant to support in-silico genome sequence analysis, being developed within the project BIOBITS. From a methodological viewpoint, the tool takes advantage of an abstraction technique similar to Temporal Abstractions, thus allowing to neglect un-relevant details. Retrieval is made flexible by the use of such multi-level abstractions, and efficient by the use of proper taxonomical index structures. Moreover, end-users are allowed to progressively relax or refine their queries, in an interactive way. A case study taken from the application domain is used to illustrate the approach.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
灵活,高效和交互式检索支持内细菌的计算机研究
研究丛枝菌根真菌(AMFs)与其共生内菌之间的相互作用对生物技术新应用的开发具有潜在的重要意义。基因组数据和合成分析是获取此类生物系统发育关系和代谢功能信息的关键技术。在本文中,我们描述了一个基于案例的检索工具,它支持定制的比较基因组学搜索,并且是模块化架构的一部分,旨在支持在BIOBITS项目中开发的硅基因组序列分析。从方法论的角度来看,该工具利用了类似于时态抽象的抽象技术,因此可以忽略不相关的细节。通过使用这种多级抽象,检索变得灵活,通过使用适当的分类索引结构,检索变得高效。此外,允许最终用户以交互的方式逐步放松或改进他们的查询。本文使用来自应用程序领域的一个案例研究来说明该方法。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Independence-Based MAP for Markov Networks Structure Discovery Flexible, Efficient and Interactive Retrieval for Supporting In-silico Studies of Endobacteria Recurrent Neural Networks for Moisture Content Prediction in Seed Corn Dryer Buildings Top Subspace Synthesizing for Promotional Subspace Mining RELIEF-C: Efficient Feature Selection for Clustering over Noisy Data
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1