Omics technologies are widely used in biomedical research. Precision medicine focuses on individual-level disease treatment and prevention. Here, we propose the usage of the term ‘precision omics’ to represent the combinatorial strategy that applies omics to translate large-scale molecular omics data for precision disease understanding and accurate disease diagnosis, treatment and prevention. Given the complexity of both omics and precision medicine, precision omics requires standardized representation and integration of heterogeneous data types. Ontology has emerged as an important artificial intelligence component to become critical for standard data and metadata representation, standardization and integration. To support precision omics, we propose a precision omics ontology hypothesis, which hypothesizes that the effectiveness of precision omics is positively correlated with the interoperability of ontologies used for data and knowledge integration. Therefore, to make effective precision omics studies, interoperable ontologies are required to standardize and incorporate heterogeneous data and knowledge in a human- and computer-interpretable manner. Methods for efficient development and application of interoperable ontologies are proposed and illustrated. With the interoperable omics data and knowledge, omics tools such as OmicsViz can also be evolved to process, integrate, visualize and analyze various omics data, leading to the identification of new knowledge and hypotheses of molecular mechanisms underlying the outcomes of diseases such as COVID-19. Given extensive COVID-19 omics research, we propose the strategy of precision omics supported by interoperable ontologies, accompanied with ontology-based semantic reasoning and machine learning, leading to systematic disease mechanism understanding and rational design of precision treatment and prevention. Short Precision medicine focuses on individual-level disease treatment and prevention. Precision omics is a new strategy that applies omics for precision medicine research, which requires standardized representation and integration of individual genetics and phenotypes, experimental conditions, and data analysis settings. Ontology has emerged as an important artificial intelligence component to become critical for standard data and metadata representation, standardization and integration. To support precision omics, interoperable ontologies are required in order to standardize and incorporate heterogeneous data and knowledge in a human- and computer-interpretable manner. With the interoperable omics data and knowledge, omics tools such as OmicsViz can also be evolved to process, integrate, visualize and analyze various omics data, leading to the identification of new knowledge and hypotheses of molecular mechanisms underlying disease outcomes. The precision COVID-19 omics study is provided as the primary use case to illustrate the rationale and implementation of the precision omics strategy.

中文翻译：

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具有可互操作本体的精密组学数据集成和分析及其在 COVID-19 研究中的应用

组学技术广泛应用于生物医学研究。精准医学专注于个体层面的疾病治疗和预防。在这里，我们建议使用“精准组学”一词来代表应用组学转化大规模分子组学数据以实现精准疾病理解和准确疾病诊断、治疗和预防的组合策略。鉴于组学和精准医学的复杂性，精准组学需要异构数据类型的标准化表示和集成。本体已成为重要的人工智能组件，对于标准数据和元数据表示、标准化和集成至关重要。为了支持精准组学，我们提出了精准组学本体假设，该假设假设精准组学的有效性与用于数据和知识集成的本体的互操作性正相关。因此，为了进行有效的精密组学研究，需要可互操作的本体以人类和计算机可解释的方式标准化和整合异构数据和知识。提出并说明了有效开发和应用可互操作本体的方法。借助可互操作的组学数据和知识，OmicsViz 等组学工具也可以发展为处理、整合、可视化和分析各种组学数据，从而识别新知识和新冠病毒等疾病结果的分子机制假设。 19. 鉴于广泛的COVID-19组学研究，我们提出了由可互操作本体支持的精准组学策略，并结合基于本体的语义推理和机器学习，从而实现系统的疾病机制理解和精准治疗和预防的合理设计。Short 精准医学专注于个体层面的疾病治疗和预防。精准组学是一种将组学应用于精准医学研究的新策略，需要个体遗传学和表型、实验条件和数据分析设置的标准化表示和整合。本体已成为重要的人工智能组件，对于标准数据和元数据表示、标准化和集成至关重要。为了支持精密组学，需要可互操作的本体，以便以人类和计算机可解释的方式标准化和整合异构数据和知识。借助可互操作的组学数据和知识，OmicsViz 等组学工具也可以发展为处理、整合、可视化和分析各种组学数据，从而识别新知识和疾病结果分子机制的假设。精准的 COVID-19 组学研究作为主要用例来说明精准组学策略的基本原理和实施。