In silico identification of phyto-therapies

植物疗法的计算机识别

• 批准号: 9117880
• 负责人: Wayne Law
• 金额: $ 37.97万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9117880
• 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; Health; 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）：已被认为是构成可以追溯到最古老文明的药物的基础。为了补充合成药物发现过程，仍然有一个重要的机会来鉴定潜在的基于植物的新疗法（“植物治疗”）。目前用于发现潜在植物治疗的方法是费力的，耗时的，并且主要是手动。数字格式中民族植物学和生物医学知识的可用性提高表明，可能有可能利用自动化技术来促进植物治疗发现过程。因此，该计划的长期目标是开发一个语义综合的框架，该框架可用于识别和验证嵌入在民族植物学和生物医学知识源中的潜在植物治疗，从而鼓励对这种知识和生物多样性的保护。整个项目围绕三个主要目标建立，这些目标是：（1）开发标准驱动的黄金标准，可用于基准基准自动化疫苗治疗识别方法； （2）开发一种自动化方法，以确定数字化生物多样性文献中潜在的植物治疗方法（生物多样性 遗产库），生物医学文献引用（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）利用矢量空间建模技术来预测潜在的植物治疗的相关性。这项努力的成功将为翻译植物知识的增长但目前脱节的证据基础的阶段奠定了基础，以阐明潜在的植物治疗的工具。此外，通过实现这些目标，该项目还将在计算机平台中建立首个型，可以扩展以从广泛的生物多样性来源中识别其他治疗剂。该项目的核心方面将以开发计算技术来弥合生物多样性和生物医学知识的经验，包括研究团队开创的那些知识。 该项目将汇集来自两个机构的生物医学信息学，图书馆科学和民族植物学经验和专业知识：佛蒙特大学和纽约植物园。该项目的多机构和多PIC方面支持了拟议项目的可行性，并将使基本任务的负载平衡，从而可以符合为每个目标设定的拟议里程碑。为此，拟议的努力的成功将建立在收集民族植物学知识，分析和联系生物多样性和生物医学知识来源的经验的基础上，以及开发系统地注释Corpora的方法，用于支持自然语言处理和数据挖掘的后续目的。