In silico identification of phyto-therapies

植物疗法的计算机识别

• 批准号: 9123422
• 负责人: Wayne Law
• 金额: $ 41.97万
• 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 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9123422
• 关键词: Address; Affect; Archives; Area; Automated Annotation; Back; Benchmarking; Biodiversity; 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; Natural Products; New York; Ontology; Peer Review; Performance; Plants; Primary Health Care; Process; PubChem; PubMed; Public Health; Publishing; 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; novel therapeutics; 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）：植物被认为是形成药物的基础，可以追溯到最古老的文明。为了补充合成药物发现过程，仍然存在从植物来源确定潜在的新疗法（“植物疗法”）的重要机会。目前用于发现潜在的植物疗法的方法是费力的，耗时的，并且大多是手动的。以数字格式提供的民族植物学和生物医学知识的增加表明，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，这一倡议的长期目标是制定一个语义综合框架，可用于识别和验证嵌入民族植物学和生物医学知识来源的潜在植物疗法，从而鼓励保护这种知识和生物多样性。整个项目围绕三个主要目标，即：（1）开发一个标准驱动的黄金标准，可用于基准自动化植物疗法识别方法;（2）开发一种自动化方法，从数字化生物多样性文献（生物多样性）中识别潜在的植物疗法。 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;以及（3）利用向量空间建模技术来预测潜在植物疗法的相关性。这一奋进的成功将为将不断增长但目前脱节的药用植物知识的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个计算机平台，该平台可以扩展到从广泛的生物多样性来源中确定其他治疗方法。该项目的核心方面将建立在开发计算技术的经验基础上，以连接生物多样性和生物医学知识，包括研究小组开创的知识。 该项目将汇集来自佛蒙特大学和纽约植物园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，拟议的奋进的成功将建立在收集民族植物学知识，分析和连接生物多样性和生物医学知识来源，并开发方法，系统地注释语料库，以支持自然语言处理和数据挖掘追求的经验的基础上。

项目成果

Harnessing Biomedical Natural Language Processing Tools to Identify Medicinal Plant Knowledge from Historical Texts.

利用生物医学自然语言处理工具从历史文本中识别药用植物知识。

• 发表时间: 2017
• 期刊: AMIA ... Annual Symposium proceedings. AMIA Symposium
• 作者: Sharma,Vivekanand;Law,Wayne;Balick,MichaelJ;Sarkar,IndraNeil
• 通讯作者: Sarkar,IndraNeil

Identifying Plant-Human Disease Associations in Biomedical Literature: A Case Study.

识别生物医学文献中的植物与人类疾病关联：案例研究。

• 发表时间: 2016
• 期刊: AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
• 作者: Sharma,Vivekanand;Law,Wayne;Balick,MichaelJ;Sarkar,IndraNeil
• 通讯作者: Sarkar,IndraNeil