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本文引用的文献

1
Interactive and fuzzy search: a dynamic way to explore MEDLINE.交互式和模糊搜索:探索 MEDLINE 的动态方式。
Bioinformatics. 2010 Sep 15;26(18):2321-7. doi: 10.1093/bioinformatics/btq414. Epub 2010 Jul 12.
2
PathText: a text mining integrator for biological pathway visualizations.PathText:一个用于生物通路可视化的文本挖掘集成器。
Bioinformatics. 2010 Jun 15;26(12):i374-81. doi: 10.1093/bioinformatics/btq221.
3
Enabling multi-level relevance feedback on PubMed by integrating rank learning into DBMS.通过将排序学习集成到 DBMS 中,实现 PubMed 上的多层次相关性反馈。
BMC Bioinformatics. 2010 Apr 16;11 Suppl 2(Suppl 2):S6. doi: 10.1186/1471-2105-11-S2-S6.
4
Text mining and manual curation of chemical-gene-disease networks for the comparative toxicogenomics database (CTD).文本挖掘和化学-基因-疾病网络的人工整理用于比较毒理学基因组数据库(CTD)。
BMC Bioinformatics. 2009 Oct 8;10:326. doi: 10.1186/1471-2105-10-326.
5
MedlineRanker: flexible ranking of biomedical literature.MedlineRanker:生物医学文献的灵活排序
Nucleic Acids Res. 2009 Jul;37(Web Server issue):W141-6. doi: 10.1093/nar/gkp353. Epub 2009 May 8.
6
PPI finder: a mining tool for human protein-protein interactions.PPI发现者:一种用于人类蛋白质-蛋白质相互作用的挖掘工具。
PLoS One. 2009;4(2):e4554. doi: 10.1371/journal.pone.0004554. Epub 2009 Feb 23.
7
SciMiner: web-based literature mining tool for target identification and functional enrichment analysis.SciMiner:用于靶点识别和功能富集分析的基于网络的文献挖掘工具。
Bioinformatics. 2009 Mar 15;25(6):838-40. doi: 10.1093/bioinformatics/btp049. Epub 2009 Feb 2.
8
A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology.通过系统生物学的群落方法获得的酵母代谢网络共识重建。
Nat Biotechnol. 2008 Oct;26(10):1155-60. doi: 10.1038/nbt1492.
9
Evaluation of text-mining systems for biology: overview of the Second BioCreative community challenge.生物学文本挖掘系统评估:第二届生物创意社区挑战赛概述
Genome Biol. 2008;9 Suppl 2(Suppl 2):S1. doi: 10.1186/gb-2008-9-s2-s1. Epub 2008 Sep 1.
10
FACTA: a text search engine for finding associated biomedical concepts.FACTA:一个用于查找相关生物医学概念的文本搜索引擎。
Bioinformatics. 2008 Nov 1;24(21):2559-60. doi: 10.1093/bioinformatics/btn469. Epub 2008 Sep 4.

生物信息学中的文献检索与挖掘:现状与挑战

Literature retrieval and mining in bioinformatics: state of the art and challenges.

作者信息

Manconi Andrea, Vargiu Eloisa, Armano Giuliano, Milanesi Luciano

机构信息

Institute for Biomedical Technologies, National Research Council, Via F.lli Cervi, 93, 20090 Segrate, Italy.

出版信息

Adv Bioinformatics. 2012;2012:573846. doi: 10.1155/2012/573846. Epub 2012 Jun 21.

DOI:10.1155/2012/573846
PMID:22778730
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3388278/
Abstract

The world has widely changed in terms of communicating, acquiring, and storing information. Hundreds of millions of people are involved in information retrieval tasks on a daily basis, in particular while using a Web search engine or searching their e-mail, making such field the dominant form of information access, overtaking traditional database-style searching. How to handle this huge amount of information has now become a challenging issue. In this paper, after recalling the main topics concerning information retrieval, we present a survey on the main works on literature retrieval and mining in bioinformatics. While claiming that information retrieval approaches are useful in bioinformatics tasks, we discuss some challenges aimed at showing the effectiveness of these approaches applied therein.

摘要

在信息的交流、获取和存储方面,世界已经发生了巨大的变化。数以亿计的人每天都参与信息检索任务,特别是在使用网络搜索引擎或搜索电子邮件时,这使得这种信息获取方式成为主导形式,超越了传统的数据库式搜索。如何处理如此海量的信息现已成为一个具有挑战性的问题。在本文中,在回顾了与信息检索相关的主要主题之后,我们对生物信息学中文献检索和挖掘的主要工作进行了综述。在声称信息检索方法在生物信息学任务中有用的同时,我们讨论了一些挑战,旨在展示这些方法在其中应用的有效性。