In silico identification of phyto-therapies

植物疗法的计算机识别

• 批准号: 8749705
• 负责人: Wayne Law
• 金额: $ 38.99万
• 依托单位: UNIVERSITY OF VERMONT & ST AGRIC COLLEGE
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8749705
• 关键词: Address; Affect; Archives; Area; Automated Annotation; Back; Benchmarking; Biodiversity; Biological Factors; Biomedical Research; Books; Botanicals; Chemicals; Civilization; Clinical Trials; Complement; Computational Technique; Computer Simulation; Data; Data Set; Development; Disease; Equilibrium; Ethnobotany; Evaluation; Expert Opinion; Foundations; Future; Genbank; Genomics; Goals; Gold; HIV; Hepatitis; Individual; Institution; Island; Knowledge; Libraries; Library Science; Link; Literature; MEDLINE; Manuals; Medicinal Plants; Medicine; Methods; Modeling; Names; Natural Language Processing; New York; Ontology; Peer Review; Performance; Plants; Primary Health Care; Process; PubChem; PubMed; Public Health; Publishing; Relative (related person); Research; Resources; Review Literature; Samoan; Source; Space Models; Staging; Surveys; System; T-Lymphocyte; Techniques; Text; Therapeutic; Therapeutic Uses; Time; Toxic effect; Translations; Treatment Protocols; Trees; Universities; Vermont; base; biomedical informatics; clinical application; computer infrastructure; data mining; digital; drug candidate; drug discovery; evidence base; experience; indexing; literature citation; meetings; prostratin; success; synthetic drug; tool; vector

DESCRIPTION (provided by applicant): Plants have been acknowledged as forming the basis of medicines dating back to the most ancient civilizations. To complement synthetic drug discovery processes, there remains a significant opportunity for identifying potential new therapies from plant-based sources ("phyto-therapies"). Current approaches used for the discovery of potential phyto-therapies are laborious, time-consuming, and mostly manual. The increased availability of ethnobotanical and biomedical knowledge in digital formats suggests that there may be the potential to leverage automated techniques to facilitate the phyto-therapy discovery process. The long-term goal of this initiative is thus to develop a semantically integrated framework that could be used to identify and validate potential phyto-therapies embedded within ethnobotanical and biomedical knowledge sources, and thus encourage the conservation of this knowledge and biodiversity. The overall project is built around three major aims, which are to: (1) develop a standards-driven gold standard that can be used for benchmarking automated phyto-therapy identification approaches; (2) develop an automated approach to identify potential phyto-therapies from digitized biodiversity literature (Biodiversity Heritage Library), biomedical literature citations (MEDLINE) or digital full-text (PubMed Central), genomic (GenBank), clinical trial (ClinicalTrials.gov), and chemical (PubChem) resources; and (3) leverage vector space modeling techniques to predict the relevance of potential phyto-therapies. The success of this endeavor will set the stage for the translation of a growing, but currently disjointed, evidence-base of medicinal plant knowledge into tools for the elucidation of potential phyto-therapies. Furthermore, through achieving these aims, this project will also establish a first-of- its-kind in silico platform that could be extended to identify additional therapeutics from a broad spectrum of biodiversity sources. The core aspects of this project will build on experience with developing computational techniques to bridge biodiversity and biomedical knowledge, including those that have been pioneered by the research team. This project will bring together biomedical informatics, library science, and ethnobotany experience and expertise from two institutions: the University of Vermont and The New York Botanical Garden. The multi- institutional and multi-PI aspects of this project support the feasibility of the proposed project aims and will furthermore enable the load-balancing of essential tasks such that they may meet the proposed milestones set for each aim. To this end, the success of the proposed endeavor will be built on a foundation of experiences in gathering ethnobotanical knowledge, analyzing and linking biodiversity and biomedical knowledge sources, and developing approaches for systematically annotating corpora for subsequent purposes in support of natural language processing and data mining pursuits.

描述（由申请人提供）：自最古老的文明以来，植物已被公认为构成药物的基础。为了补充合成药物的发现过程，仍然存在从植物来源（“植物疗法”）中识别潜在新疗法的重要机会。目前用于发现潜在植物疗法的方法费力、耗时，而且大多是手动的。数字格式的民族植物学和生物医学知识的可用性的增加表明，可能有潜力利用自动化技术来促进植物疗法的发现过程。因此，该倡议的长期目标是开发一个语义集成框架，可用于识别和验证嵌入民族植物学和生物医学知识来源中的潜在植物疗法，从而鼓励保护这些知识和生物多样性。整个项目围绕三个主要目标构建，即：（1）开发标准驱动的黄金标准，可用于对自动化植物疗法识别方法进行基准测试； (2) 开发一种自动化方法，从数字化生物多样性文献（Biodiversity Heritage Library）、生物医学文献引文（MEDLINE）或数字全文（PubMed Central）， 基因组 (GenBank)、临床试验 (ClinicalTrials.gov) 和化学 (PubChem) 资源； (3) 利用向量空间建模技术来预测潜在植物疗法的相关性。这项努力的成功将为将不断增长但目前支离破碎的药用植物知识证据基础转化为阐明潜在植物疗法的工具奠定基础。此外，通过实现这些目标，该项目还将建立一个首创的计算机平台，该平台可以扩展以从广泛的生物多样性来源中识别其他治疗方法。该项目的核心方面将建立在开发计算技术以连接生物多样性和生物医学知识的经验之上，包括研究团队开创的技术。 该项目将汇集来自佛蒙特大学和纽约植物园这两个机构的生物医学信息学、图书馆学和民族植物学经验和专业知识。该项目的多机构和多PI方面支持拟议项目目标的可行性，并将进一步实现基本任务的负载平衡，以便它们可以满足为每个目标设定的拟议里程碑。为此，拟议努力的成功将建立在收集民族植物学知识、分析和链接生物多样性和生物医学知识源以及开发系统注释语料库的方法以支持自然语言处理和数据挖掘的后续目的的经验基础上。