TEXT MINING FOR ANTIMICROBIAL PEPTIDES

抗菌肽的文本挖掘

• 批准号: 7627610
• 负责人: ZHENGHIN CHEN
• 金额: $ 5.18万
• 依托单位: UNIVERSITY OF NEBRASKA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-05-01 至 2008-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7627610
• 关键词: Antibiotics; Area; Attention; Communicable Diseases; Computer Retrieval of Information on Scientific Projects Database; Databases; Funding; Genes; Grant; Information Retrieval; Institution; Literature; Methods; Numbers; Peptides; Personal Satisfaction; PubMed; Research; Research Personnel; Resources; Source; United States National Institutes of Health; antimicrobial peptide; base; pathogen; text searching

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Antimicrobial peptides are small peptides encoded by genes. The research area of antimicrobial peptides has attracted intense attention in recent years because their potential use in the cure of infectious diseases caused by pathogens that have become counteractive to traditional antibiotics. There exist huge amounts of antimicrobial peptides research articles and this number is continuously increasing. Although some biomedical databases, such as PubMed, have been well established, they provide only query-based information retrieval and end-users need to manually find out relevant information from thousands of retrieved articles. In this project, we investigate various document clustering as well as other text mining methods for antimicrobial peptides literature.

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这不一定是调查员的机构。 抗菌肽是由基因编码的小肽。 近年来，抗菌肽的研究领域引起了极大的关注，因为它们在治愈传统疾病中的潜在使用是由病原体引起的，这些病原体已与传统的抗生素相反。 存在大量的抗菌肽研究文章，并且该数字不断增加。尽管某些生物医学数据库（例如PubMed）已经建立了良好，但它们仅提供基于查询的信息检索，最终用户需要手动从数千个检索的文章中查找相关信息。 在该项目中，我们研究了各种文档聚类以及其他文本挖掘方法，用于抗菌肽文献。