The last few years have seen tremendous growth in human microbiome research, with a particular focus on the links to both mental and physical health and disease. Medical and experimental settings provide initial sources of information about these links, but individual studies produce disconnected pieces of knowledge bounded in context by the perspective of expert researchers reading full-text publications. Building a knowledge base (KB) consolidating these disconnected pieces is an essential first step to democratize and accelerate the process of accessing the collective discoveries of human disease connections to the human microbiome. In this article, we survey the existing tools and development efforts that have been produced to capture portions of the information needed to construct a KB of all known human microbiome-disease associations and highlight the need for additional innovations in natural language processing (NLP), text mining, taxonomic representations, and field-wide vocabulary standardization in human microbiome research. Addressing these challenges will enable the construction of KBs that help identify new insights amenable to experimental validation and potentially clinical decision support.

中文翻译：

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人类微生物组-疾病关联知识库构建的挑战。


过去几年，人类微生物组研究取得了巨大发展，特别关注与心理和身体健康及疾病的联系。医学和实验环境提供了有关这些联系的初始信息来源，但个别研究产生了一些互不相关的知识片段，这些知识片段受到专家研究人员阅读全文出版物的视角的限制。建立一个知识库（KB）来整合这些互不相关的部分，是民主化和加速获取人类疾病与人类微生物组之间联系的集体发现过程的重要第一步。在本文中，我们调查了现有的工具和开发工作，这些工具和开发工作旨在捕获构建所有已知人类微生物组与疾病关联的知识库所需的部分信息，并强调在自然语言处理 (NLP) 方面进行额外创新的必要性，人类微生物组研究中的文本挖掘、分类学表示和全领域词汇标准化。解决这些挑战将有助于构建知识库，帮助识别适合实验验证和潜在临床决策支持的新见解。