MedPortal:面向精准医学的生物医学本体资源存储和应用平台

在过去10余年中,本体广泛应用于生物医学数据分析、检索、整合和再利用中。本体作为一种特殊类型的数据资源,数据量也在迅速增加。为了促进精准医疗领域数据集的整合,并为国内用户提供本体数据资源服务,构建MedPortal本体资源存储和应用平台。通过复用NCBO BioPortal技术,搭建MedPotal软件框架。遴选精准医学相关本体,建立本体资源库。对原框架中的代码和本体处理工具进行修正和完善,使之能够在本体稳定运行的基础上满足大批量数据的自动化处理。目前,该平台已整合42个生物医学本体,建立了本体之间术语映射关系,通过页面和REST API方式,提供术语检索、本体映射、数据标准化注释等本体应用服务(http://medportal.bmicc.cn)。MedPortal本体平台将为生物医学数据整合提供帮助。     

基于Web Service的生物学实体映射数据库

生物学实体映射就是要实现对基因、蛋白质、小分子物质、化合物和药物等实体的不同标识符和名称之间的相互转换.生物学实体映射可以帮助生物医学研究者将实验结果关联到海量的在线生物医学数据资源,并为生物医学文本挖掘和信息检索的研究者在命名实体识别和查询关键词扩展方面提供技术支持.构建一个生物学实体映射数据库,其中存储了大量的生物学实体映射信息;还构建一个基于Web Service的生物学实体映射网络应用系统,为用户同时提供通过浏览器和通过Web Service的两种方式访问生物学实体映射数据库.     

基于本体的医学知识库构建及应用

目的为了充分有效地管理和利用医学知识资源,本体论被引入到医学知识组织当中。本文描述了医学本体知识库的构建步骤,构建出包括中医本体和西医本体两大分支的医学本体框架。方法采用NKI（national knowledge infrastructure）本体描述语言,结合已有的医学知识,分4级举例给出冠心病类的详细设计过程,引入美国斯坦福大学的大型本体构建工具Protege,并使用Protege2000构建冠心病医学知识体系,同时进行了初步的应用。结果通过对比已有的医学知识组织形式,本文所建立的医学本体系统具有可行性、可扩展性和可重用性的特点。结论基于本体的医学知识库将可能有效解决医学知识工程领域的多个问题。     

健康行为改变干预本体的构建与应用

生活方式干预是慢病管理的重要环节,随着互联网的发展,基于移动医疗技术的综合干预方案成为慢病管理模式研究的新趋势。面对干预复杂性和综合性越来越强的挑战,一个标准、细致、综合的框架有利于对干预方案进行解构分析,从而促进干预的质量和效果提升。本研究提出健康行为改变干预本体,通过内容分析法对干预内容进行分类提取,得到行为改变技术及其属性的重要术语集合,通过七步法结合OWL建模语言完成本体构建;并以面向高血压院外管理的干预方案为例进行验证,评估其干预方案。所得术语集合包括22个适用于基于移动医疗技术的慢病管理饮食和运动场景的行为改变技术,以及102个行为改变技术实施过程属性,健康行为改变干预本体共有128个类,51个数据属性,16个对象属性。基于本体将高血压干预方案转换为层次清晰、过程明确的干预单元组合,对其评估结果表明方案使用行为改变技术共14种,覆盖率为63.64%。该本体能够应用于慢病管理相关场景的干预设计、描述和分析评估等环节,有利于知识的组织与共享。     

本体支持的生物医学领域元数据异质性与可兼容性研究

利用本体支持数据元素的表示,是提升元数据机器可理解性的重要手段。采用生物医学通用数据元素数据库caDSR中的数据,评价相关的数据元素之间的语义异质性,并利用机器学习对元数据可兼容性进行判别。首先,从caDSR 中选取60对通用数据元素,涉及人口学、生活方式、既往病史和实验室测量等方面。依据ISO/IEC 111179标准抽提数据元素的必要组分,利用NCIT的本体支持,就每对关联数据元素的相似度进行评价。依据数据元素内部各组分的语义相似度,利用支持向量机,对数据元素间的可兼容性做出预测,其准确度超过80%。研究结果显示,目前在caDSR数据库中,对于元数据的定义存在较大的异质性,这些异质性在数据元素的概念域尤其集中。虽然如此,通过机器学习的方法,还是能够依据现有的数据元素的定义实现数据可兼容性的自动判断。研究所建立的方法,对于优化数据元素构建流程、丰富数据标准化工具具有一定的应用价值。     

跨多机构多医院的生物医学影像研究平台(e-Science)的设计

目的 近来,愈来愈多的影像信息研究人员和工程师渴望构建影像信息的基础设施或新的框架以利于医学研究人员、临床医生、生物医学工程师在一个安全有效透明的环境中进行多学科合作研究.该文介绍了在上海建立的用于生物医学影像信息研究与应用的e-Science平台的梗概与初步设计,平台的设计理念、设计策略及初步结果,并讨论在建立该平台过程中遇到的若干挑战性问题和解决的对策.     

学习科学相关基因表型Ontology的构建及应用

在MeSH-2004的基础上,应用数据库和动态网页编程技术,构建了一个学习科学相关基因表型Ontology。它的内容涵盖了行为、心理及精神疾病等与学习科学有关的基因表型,为学习科学的相关研究提供了一个规范、系统、结构化的术语系统。将该Ontology集成到各相关系统中,可以把异构数据整合起来,不但能提高相关信息的收集、管理和检索效率,还为相关信息的表达、系统间的信息交流、共享以及数据挖掘提供了一个统一的平台。     

乳腺超声图像本体构建研究

目的乳腺超声图像本体有助于乳腺超声图像语义标注、智能检索等。本文以乳腺超声图像为例,论述了乳腺超声图像的本体模型构建方法。方法首先通过主题词与语料高频词结合的方法确定乳腺超声图像本体的概念,然后借鉴UMLS提炼乳腺超声图像本体的语义关系。结果本研究构建的乳腺超声图像本体具有1274个概念,56种语义关系,通过PROGTéGé构建了乳腺超声图像本体。结论以主题词与语料高频词结合的方法确定的本体概念具有较好的乳腺超声图像语义刻画效果,本文所述的乳腺超声图像本体构建方法也适用于其他领域本体的构建。     

Protégé在构建中医治则治法本体中的运用

本文探讨了中医治则治法的逻辑框架；初步建立了中医治则治法的本体，讨论了其与高成勉等的中医学顶层本体的关系；在此基础上探索了利用Protege3．3建立本体的方法；最后本文还对中医治则治法的形式化作了原则性的探索。     

临床检验诊断学领域本体构建及其在门户网站信息检索中应用研究

本文对关键词检索和语义检索进行了比较,构建了临床检验诊断学领域本体,设计并实现了针对临床检验诊断学门户网站的语义信息检索,并进行了验证试验,结果符合预期.     

Summarizing and visualizing structural changes during the evolution of biomedical ontologies using a Diff Abstraction Network

Biomedical ontologies are a critical component in biomedical research and practice. As an ontology evolves, its structure and content change in response to additions, deletions and updates. When editing a biomedical ontology, small local updates may affect large portions of the ontology, leading to unintended and potentially erroneous changes. Such unwanted side effects often go unnoticed since biomedical ontologies are large and complex knowledge structures. Abstraction networks, which provide compact summaries of an ontology’s content and structure, have been used to uncover structural irregularities, inconsistencies and errors in ontologies. In this paper, we introduce Diff Abstraction Networks (“Diff AbNs”), compact networks that summarize and visualize global structural changes due to ontology editing operations that result in a new ontology release. A Diff AbN can be used to support curators in identifying unintended and unwanted ontology changes. The derivation of two Diff AbNs, the Diff Area Taxonomy and the Diff Partial-area Taxonomy, is explained and Diff Partial-area Taxonomies are derived and analyzed for the Ontology of Clinical Research, Sleep Domain Ontology, and eagle-i Research Resource Ontology. Diff Taxonomy usage for identifying unintended erroneous consequences of quality assurance and ontology merging are demonstrated.     

A unified software framework for deriving,visualizing, and exploring abstraction networks for ontologies

Software tools play a critical role in the development and maintenance of biomedical ontologies. One important task that is difficult without software tools is ontology quality assurance. In previous work, we have introduced different kinds of abstraction networks to provide a theoretical foundation for ontology quality assurance tools. Abstraction networks summarize the structure and content of ontologies. One kind of abstraction network that we have used repeatedly to support ontology quality assurance is the partial-area taxonomy. It summarizes structurally and semantically similar concepts within an ontology. However, the use of partial-area taxonomies was ad hoc and not generalizable. In this paper, we describe the Ontology Abstraction Framework (OAF), a unified framework and software system for deriving, visualizing, and exploring partial-area taxonomy abstraction networks. The OAF includes support for various ontology representations (e.g., OWL and SNOMED CT’s relational format). A Protégé plugin for deriving “live partial-area taxonomies” is demonstrated.     

ONTOFUSION: ontology-based integration of genomic and clinical databases

ONTOFUSION is an ontology-based system designed for biomedical database integration. It is based on two processes: mapping and unification. Mapping is a semi-automated process that uses ontologies to link a database schema with a conceptual framework-named virtual schema. There are three methodologies for creating virtual schemas, according to the origin of the domain ontology used: (1) top-down--e.g. using an existing ontology, such as the UMLS or Gene Ontology--, (2) bottom-up--building a new domain ontology-- and (3) a hybrid combination. Unification is an automated process for integrating ontologies and hence the database to which they are linked. Using these methods, we employed ONTOFUSION to integrate a large number of public genomic and clinical databases, as well as biomedical ontologies.     

Dynamic sub-ontology evolution for traditional Chinese medicine web ontology

As a form of important domain knowledge, large-scale ontologies play a critical role in building a large variety of knowledge-based systems. To overcome the problem of semantic heterogeneity and encode domain knowledge in reusable format, a large-scale and well-defined ontology is also required in the traditional Chinese medicine discipline. We argue that to meet the on-demand and scalability requirement ontology-based systems should go beyond the use of static ontology and be able to self-evolve and specialize for the domain knowledge they possess. In particular, we refer to the context-specific portions from large-scale ontologies like the traditional Chinese medicine ontology as sub-ontologies. Ontology-based systems are able to reuse sub-ontologies in local repository called ontology cache. In order to improve the overall performance of ontology cache, we propose to evolve sub-ontologies in ontology cache to optimize the knowledge structure of sub-ontologies. Moreover, we present the sub-ontology evolution approach based on a genetic algorithm for reusing large-scale ontologies. We evaluate the proposed evolution approach with the traditional Chinese medicine ontology and obtain promising results.     

Discovering Beaten Paths in Collaborative Ontology-Engineering Projects using Markov Chains

Biomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases as a taxonomy or the National Cancer Institute Thesaurus as an OWL-based ontology, play a critical role in acquiring, representing and processing information about human health. With increasing adoption and relevance, biomedical ontologies have also significantly increased in size. For example, the 11th revision of the International Classification of Diseases, which is currently under active development by the World Health Organization contains nearly $50\text{,}000$ classes representing a vast variety of different diseases and causes of death. This evolution in terms of size was accompanied by an evolution in the way ontologies are engineered. Because no single individual has the expertise to develop such large-scale ontologies, ontology-engineering projects have evolved from small-scale efforts involving just a few domain experts to large-scale projects that require effective collaboration between dozens or even hundreds of experts, practitioners and other stakeholders. Understanding the way these different stakeholders collaborate will enable us to improve editing environments that support such collaborations. In this paper, we uncover how large ontology-engineering projects, such as the International Classification of Diseases in its 11th revision, unfold by analyzing usage logs of five different biomedical ontology-engineering projects of varying sizes and scopes using Markov chains. We discover intriguing interaction patterns (e.g., which properties users frequently change after specific given ones) that suggest that large collaborative ontology-engineering projects are governed by a few general principles that determine and drive development. From our analysis, we identify commonalities and differences between different projects that have implications for project managers, ontology editors, developers and contributors working on collaborative ontology-engineering projects and tools in the biomedical domain.     

OntoVIP: An ontology for the annotation of object models used for medical image simulation

This paper describes the creation of a comprehensive conceptualization of object models used in medical image simulation, suitable for major imaging modalities and simulators. The goal is to create an application ontology that can be used to annotate the models in a repository integrated in the Virtual Imaging Platform (VIP), to facilitate their sharing and reuse. Annotations make the anatomical, physiological and pathophysiological content of the object models explicit. In such an interdisciplinary context we chose to rely on a common integration framework provided by a foundational ontology, that facilitates the consistent integration of the various modules extracted from several existing ontologies, i.e. FMA, PATO, MPATH, RadLex and ChEBI. Emphasis is put on methodology for achieving this extraction and integration. The most salient aspects of the ontology are presented, especially the organization in model layers, as well as its use to browse and query the model repository.     

Semantic SenseLab: Implementing the vision of the Semantic Web in neuroscience

ObjectiveIntegrative neuroscience research needs a scalable informatics framework that enables semantic integration of diverse types of neuroscience data. This paper describes the use of the Web Ontology Language (OWL) and other Semantic Web technologies for the representation and integration of molecular-level data provided by several of SenseLab suite of neuroscience databases.MethodsBased on the original database structure, we semi-automatically translated the databases into OWL ontologies with manual addition of semantic enrichment. The SenseLab ontologies are extensively linked to other biomedical Semantic Web resources, including the Subcellular Anatomy Ontology, Brain Architecture Management System, the Gene Ontology, BIRNLex and UniProt. The SenseLab ontologies have also been mapped to the Basic Formal Ontology and Relation Ontology, which helps ease interoperability with many other existing and future biomedical ontologies for the Semantic Web. In addition, approaches to representing contradictory research statements are described. The SenseLab ontologies are designed for use on the Semantic Web that enables their integration into a growing collection of biomedical information resources.ConclusionWe demonstrate that our approach can yield significant potential benefits and that the Semantic Web is rapidly becoming mature enough to realize its anticipated promises. The ontologies are available online at http://neuroweb.med.yale.edu/senselab/.     

A methodology for extending domain coverage in SemRep

We describe a domain-independent methodology to extend SemRep coverage beyond the biomedical domain. SemRep, a natural language processing application originally designed for biomedical texts, uses the knowledge sources provided by the Unified Medical Language System (UMLS^©). Ontological and terminological extensions to the system are needed in order to support other areas of knowledge. We extended SemRep’s application by developing a semantic representation of a previously unsupported domain. This was achieved by adapting well-known ontology engineering phases and integrating them with the UMLS knowledge sources on which SemRep crucially depends. While the process to extend SemRep coverage has been successfully applied in earlier projects, this paper presents in detail the step-wise approach we followed and the mechanisms implemented. A case study in the field of medical informatics illustrates how the ontology engineering phases have been adapted for optimal integration with the UMLS. We provide qualitative and quantitative results, which indicate the validity and usefulness of our methodology.     

Desiderata for domain reference ontologies in biomedicine

Domain reference ontologies represent knowledge about a particular part of the world in a way that is independent from specific objectives, through a theory of the domain. An example of reference ontology in biomedical informatics is the Foundational Model of Anatomy (FMA), an ontology of anatomy that covers the entire range of macroscopic, microscopic, and subcellular anatomy. The purpose of this paper is to explore how two domain reference ontologies--the FMA and the Chemical Entities of Biological Interest (ChEBI) ontology, can be used (i) to align existing terminologies, (ii) to infer new knowledge in ontologies of more complex entities, and (iii) to manage and help reasoning about individual data. We analyze those kinds of usages of these two domain reference ontologies and suggest desiderata for reference ontologies in biomedicine. While a number of groups and communities have investigated general requirements for ontology design and desiderata for controlled medical vocabularies, we are focusing on application purposes. We suggest five desirable characteristics for reference ontologies: good lexical coverage, good coverage in terms of relations, compatibility with standards, modularity, and ability to represent variation in reality.     

Ontology integration: experience with medical terminologies

To build a common controlled vocabulary is a formidable challenge in medical informatics. Due to vast scale and multiplicity in interpretation of medical data, it is natural to face overlapping terminologies in the process of practicing medical informatics [A. Rector, Clinical terminology: why is it so hard? Methods Inf. Med. 38 (1999) 239–252]. A major concern lies in the integration of seemingly overlapping terminologies in the medical domain and this issue has not been well addressed. In this paper, we describe a novel approach for medical ontology integration that relies on the theory of Algorithmic Semantic Refinement we previously developed. Our approach simplifies the task of matching pairs of corresponding concepts derived from a pair of ontologies, which is vital to terminology mapping. A formal theory and algorithm for our approach have been devised and the application of this method to two medical terminologies has been developed. The result of our work is an integrated medical terminology and a methodology and implementation ready to use for other ontology integration tasks.