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

该项目的更广泛的影响/商业潜力是加速研究和开发的步伐，以便更快地将技术部署到商业/工业环境中。在许多领域，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变得越来越困难。此外，内容扩展得如此之快，以至于大多数领域都在迅速形成子学科，导致“筒仓化”。不同的知识子领域，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和呈现信息给用户。目前的搜索引擎也有缺点，主要是搜索结果过于相似。此外，虽然这些引擎提供了与已知搜索目标相关的信息，但对于用户从未听说过但可能有用的信息，它们在提供意想不到的结果方面效率较低。因此，我们需要的是一个探索系统，它能给搜索者提供一个强大的偶然元素，最大限度地从各种各样、意想不到的、潜在的挑衅来源中获得结果。这将为不同学科之间的知识转移提供一种快速、相关的手段，从而打破孤岛，促进跨学科创新。设计这个系统是为了提供一种系统的、自动的发现手段。这个小企业创新研究（SBIR）第二阶段项目的重点是优化和扩展偶然文档搜索系统，通过类比将技术重新用于横向领域。通过将离散的内容分解为本体可分离的实体，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。该方法为药物再利用提供了重要的价值主张，这是本项目当前的重点。为了扩展数百万个文档的成对比较和网络组装，将开发一个基于地图缩减的文本处理框架，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，以实现新兴文档关系网络的快速查询。然后将使用一系列机器学习算法来确定文档关系网络中潜在的隐藏结构架构特征。机器学习将通过分析节点间关系和子图基序(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。

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.