SBIR Phase II: Serendipitous Search System Using Lateral Analogy to Match Potential Solutions to Unmet Needs:Feasibility Study Based on Screening Approved Drugs for Repurposing

SBIR 第二阶段：利用横向类比将潜在解决方案与未满足需求相匹配的偶然搜索系统：基于筛选已批准药物再利用的可行性研究

• 批准号: 1430780
• 负责人: Brian Sager
• 金额: $ 67.56万
• 依托单位: Leonardo Innovations Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-12-01 至 2019-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1430780&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Serendipitous; Search

The broader impact/commercial potential of this project is to accelerate the paceof research and development to enable more rapid deployment of technologiesinto commercial / industrial contexts. In many fields, information is expanding atsuch an exponential rate that finding relevant results to technical knowledgesearches is increasingly difficult. Further, content is expanding so fast that mostfields are rapidly forming sub-disciplines, leading to the ?silo-ing? of differentknowledge sub-domains, a clear challenge to both academia and industry. Weneed ever better ways to organize and present information to users. There aredisadvantages of the current search engines, mostly relating to excessivesimilarity in search results. Further, while these engines present informationrelating to a known search target, they are less effective at presentingunexpected results for information that a user has never heard of but that wouldbe useful. What is therefore needed is an exploration system giving searchers astrong serendipitous element with a maximum likelihood of results from diverse,unexpected, and potentially provocative sources. This will break down silos byproviding a rapid, relevant means for knowledge-transfer between differentdisciplines, fostering interdisciplinary innovation. This system has been designedto provide a means for systematic, automated discovery.This Small Business Innovation Research (SBIR) Phase 2 project is focused onoptimizing and scaling a serendipitous document search system for repurposingtechnologies by analogy into lateral fields. Both by sub-parsing discrete contentinto ontologically separable entities, such as capability, characteristic, andcomposition, and by comparatively assessing certain of these attributes betweensuch entities, the attribute relatedness of these entities can be used to drive theirself-assembly into related attribute networks. This approach provides asignificant value proposition for drug repurposing, which is the current focus ofthis project. To scale the pair-wise comparison and network assembly of millionsof documents, a map-reduce based text-processing framework will be developedso that massively parallel computations can be carried out in a time- and costefficientmanner. A distributed search engine technology will be deployed toenable rapid querying of the emerging document relationship network. A series ofmachine learning algorithms will then be used to determine potentially hiddenstructural architectural features within the document relationship network.Machine learning will elucidate the nature of the relationships in drug networksthrough analyses of inter-node relationships and sub-graph motifs (termed?innovation motifs?). Documents including U.S. patents and scientific papers willbe processed in the system.

该项目更广泛的影响/商业潜力是加快研究和开发的步伐，使技术更快地部署到商业/工业环境中。在许多领域中，信息正以指数级的速度增长，以至于找到与技术知识研究相关的结果越来越困难。此外，内容扩展得如此之快，大多数领域正在迅速形成子学科，导致？筒仓？不同的知识子领域，这对学术界和工业界都是一个明显的挑战。我们需要更好的方式来组织信息并将其呈现给用户。目前的搜索引擎存在着一些不足，主要表现在搜索结果的相似度过高.此外，虽然这些引擎呈现与已知搜索目标相关的信息，但它们在呈现用户从未听说过但有用的信息的意外结果方面不太有效。因此，我们需要的是一个探索系统，它能给搜索者一个强有力的偶然因素，最大可能地从各种各样的、意想不到的和潜在的挑衅性来源中获得结果。这将打破孤岛，为不同学科之间的知识转移提供快速、相关的手段，促进跨学科创新。该系统旨在提供一种系统化、自动化的发现方法。这一小型企业创新研究（SBIR）第2阶段项目的重点是优化和扩展一个偶然发现的文档搜索系统，用于通过类比将技术重新引入横向领域。通过将离散的内容分解为本体上可分离的实体，如能力、特征和组成，并通过比较评估这些实体之间的某些属性，这些实体的属性相关性可以用来驱动它们自组装成相关的属性网络。这种方法为药物再利用提供了一个重要的价值主张，这是本项目目前的重点。为了扩展数百万文档的成对比较和网络组装，将开发一个基于map-reduce的文本处理框架，以便以时间和成本效益的方式进行大规模并行计算。将部署分布式搜索引擎技术，以实现对新兴文档关系网络的快速查询。一系列的机器学习算法将被用来确定潜在的隐藏在文档关系网络中的结构架构特征。机器学习将通过分析节点间的关系和子图基序（称为？创新主题？）。包括美国专利和科学论文在内的文件将在该系统中处理。