Collaborative Research: ABI Innovation: Enabling machine-actionable semantics for comparative analyses of trait evolution

合作研究：ABI 创新：启用机器可操作的语义以进行特征进化的比较分析

• 批准号: 2048296
• 负责人: Wasila Dahdul
• 金额: $ 7.35万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-22 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2048296&HistoricalAwards=false
• 关键词: Collaborative; Research; ABI; Innovation; Enabling

The millions of species that inhabit the planet all have distinct biological traits that enable them to successfully compete in or adapt to their ecological niches. Determining accurately how these traits evolved is thus fundamental to understanding earth's biodiversity, and to predicting how it might change in the future in response to changes in ecosystems. Although sophisticated analytical methods and tools exist for analyzing traits comparatively, applying their full power to the myriad of trait observations recorded in the form of natural language descriptions has been hindered by the difficulty of allowing these tools to understand even the most basic facts implied by an unstructured free-text statement made by a human observer. The technological arsenal needed to overcome this challenge is now in principle available, thanks to a number of recent breakthroughs in the areas of knowledge representation and machine reasoning, but these technologies are challenging enough to deploy, orchestrate, and use that the barriers to effectively exploit them remains far too high for most tools. This project will create infrastructure that will dramatically reduce this barrier, with the goal of providing comparative trait analysis tools easy access to algorithms powered by machines reasoning with and making inferences from the meaning of trait descriptions. Similar to how Google, IBM Watson, and others have enabled developers of smartphone apps to incorporate, with only a few lines of code, complex machine-learning and artificial intelligence capabilities such as sentiment analysis, this project will demonstrate how easy access to knowledge computing opens up new opportunities for analysis, tools, and research. It will do this by addressing three long-standing limitations in comparative studies of trait evolution: recombining trait data, modeling trait evolution, and generating testable hypotheses for the drivers of trait adaptation.The treasure trove of morphological data published in the literature holds one of the keys to understanding the biodiversity of phenotypes, but exploiting the data in full through modern computational data science analytics remains severely hampered by the steep barriers to connecting the data with the accumulated body of morphological knowledge in a form that machines can readily act on. This project aims to address this barrier by creating a centralized computational infrastructure that affords comparative analysis tools the ability to compute with morphological knowledge through scalable online application programming interfaces (APIs), enabling developers of comparative analysis tools, and therefore their users, to tap into machine reasoning-powered capabilities and data with machine-actionable semantics. By shifting all the heavy-lifting to this infrastructure, tools can programmatically obtain answers to knowledge-based questions that would otherwise require careful study by a human export, such as objectively and reproducibly assessing the relatedness, independence, and distinctness of characters and character states, with only a few lines of code. To accomplish this, the project will adapt key products and know-how developed by the Phenoscape project, including an integrative knowledgebase of ontology-linked phenotype data, metrics for quantifying the semantic similarity of phenotype descriptions, and algorithms for synthesizing morphological data from published trait descriptions. To drive development of the computational infrastructure and to demonstrate its enabling value, the project's objectives focus on addressing three concrete long-standing needs for which the difficulty of computing with domain knowledge is the major impediment: (1) computationally synthesizing, calibrating, and assessing morphological trait matrices from across studies; (2) objectively and reproducibly incorporating morphological domain knowledge provided by ontologies into evolutionary models of trait evolution; and (3) generating testable hypotheses for adaptive diversification by incorporating semantic phenotypes into ancestral state reconstruction and identifying domain ontology concepts linked to evolutionary changes in a branch or clade more frequently than expected by chance. In addition, to better prepare evolutionary biologist users and developers of comparative analysis tools for adopting these new capabilities, a domain-tailored short-course on requisite knowledge representation and computational inference technologies will be developed and taught. More information on this project can be found at http://cate.phenoscape.org/.

居住在地球上的数以百万计的物种都具有独特的生物特征，使它们能够成功地竞争或适应其生态位。因此，准确确定这些特征是如何进化的，对于了解地球的生物多样性以及预测它在未来如何因生态系统的变化而发生变化至关重要。尽管存在用于比较分析特征的复杂分析方法和工具，但将它们的全部功能应用于以自然语言描述形式记录的无数特征观察受到了阻碍，因为这些工具很难理解特征所隐含的最基本的事实。由人类观察者做出的非结构化自由文本声明。由于知识表示和机器推理领域最近取得了一些突破，克服这一挑战所需的技术手段现在原则上已经可用，但这些技术的部署、编排和使用具有足够的挑战性，因此有效利用的障碍对于大多数工具来说，它们仍然太高。该项目将创建基础设施，大大减少这一障碍，目标是提供比较性状分析工具，方便访问由机器支持的算法，对性状描述的含义进行推理和推断。与谷歌、IBM Watson 和其他公司如何让智能手机应用程序开发人员仅用几行代码就可以整合复杂的机器学习和人工智能功能（例如情感分析）类似，该项目将展示如何轻松访问知识计算为分析、工具和研究开辟了新的机会。它将通过解决性状进化比较研究中三个长期存在的局限性来实现这一目标：重组性状数据、建模性状进化以及为性状适应的驱动因素生成可检验的假设。文献中发表的形态学数据宝库包含以下之一：理解表型生物多样性的关键，但通过现代计算数据科学分析充分利用数据仍然受到将数据与积累的形态学知识以机器可以轻松行动的形式连接起来的巨大障碍的严重阻碍 在。该项目旨在通过创建一个集中式计算基础设施来解决这一障碍，该基础设施为比较分析工具提供了通过可扩展的在线应用程序编程接口（API）使用形态学知识进行计算的能力，使比较分析工具的开发人员及其用户能够利用机器推理驱动的能力和具有机器可操作语义的数据。通过将所有繁重的工作转移到这个基础设施上，工具可以以编程方式获得基于知识的问题的答案，否则这些问题需要人工导出进行仔细研究，例如客观且可重复地评估角色和角色状态的相关性、独立性和独特性，只需几行代码。为了实现这一目标，该项目将采用 Phenoscape 项目开发的关键产品和专有技术，包括与本体相关的表型数据的综合知识库、用于量化表型描述的语义相似性的指标以及用于从已发布的性状合成形态数据的算法描述。为了推动计算基础设施的发展并展示其赋能价值，该项目的目标侧重于解决三个具体的长期需求，其中领域知识计算的难度是主要障碍：（1）计算综合、校准和评估跨研究的形态特征矩阵； （2）客观地、可重复地将本体提供的形态领域知识纳入性状进化的进化模型中； （3）通过将语义表型纳入祖先状态重建中，并识别与分支或进化枝中进化变化相关的领域本体概念，比偶然预期更频繁地生成可测试的适应性多样化假设。此外，为了更好地为进化生物学家用户和比较分析工具开发人员采用这些新功能做好准备，将开发和教授有关必要知识表示和计算推理技术的特定领域短期课程。有关该项目的更多信息，请访问 http://cate.phenoscape.org/。

项目成果

Assessing Bayesian Phylogenetic Information Content of Morphological Data Using Knowledge From Anatomy Ontologies

使用解剖学本体知识评估形态学数据的贝叶斯系统发育信息内容

• DOI: 10.1093/sysbio/syac022
• 发表时间: 2022-03
• 期刊: Systematic Biology
• 影响因子: 6.5
• 作者: Porto, Diego S.;Dahdul, Wasila M.;Lapp, Hilmar;Balhoff, James P.;Vision, Todd J.;Mabee, Paula M.;Uyeda, Josef
• 通讯作者: Uyeda, Josef

Integration of anatomy ontology data with protein–protein interaction networks improves the candidate gene prediction accuracy for anatomical entities

• DOI: 10.1186/s12859-020-03773-2
• 发表时间: 2020-10-07
• 期刊: BMC Bioinformatics
• 影响因子: 3
• 作者: Fernando PC;Mabee PM;Zeng E
• 通讯作者: Zeng E