生物医学数据集成中大规模语义映射的组装和推理。

IF 5.4
Charles Tapley Hoyt, Klas Karis, Benjamin M Gyori
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引用次数: 0

摘要

动机:数百种资源将标识符分配给生物医学概念,包括基因、小分子、生物过程、疾病和细胞类型。通常,这些资源通过将标识符分配给相同或相关的概念而重叠。这造成了数据互操作性瓶颈,因为集成使用来自不同资源的相同概念的标识符的数据集和知识库需要将这些标识符相互映射。然而,可用的映射在单个资源之间是不完整和碎片化的,这促使它们进行大规模集成。结果:我们开发了SeMRA,这是一种软件工具,可以将来自多个源的映射集成到图形数据结构中。使用图形算法,它可以推断出可用映射所隐含的缺失映射,同时跟踪来源和可信度。这允许连接标识符空间,而以前不可能进行直接映射。SeMRA实现了一个可定制的工作流,该工作流将声明性规范作为描述源的输入,以与其他配置参数集成。我们使用SeMRA生成了SeMRA原始映射数据库,它汇集了来自127个来源的4340万个映射,这些映射共同覆盖了来自445个本体和数据库的标识符。我们还描述了特定用例的基准,例如在资源分类疾病和细胞类型之间集成映射。可用性:该代码在MIT许可下可在https://github.com/biopragmatics/semra上获得。由SeMRA组装的SeMRA原始映射数据库可在https://doi.org/10.5281/zenodo.11082038.Supplementary information获得;补充数据可在Bioinformatics在线获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assembly and reasoning over semantic mappings at scale for biomedical data integration.

Motivation: Hundreds of resources assign identifiers to biomedical concepts including genes, small molecules, biological processes, diseases, and cell types. Often, these resources overlap by assigning identifiers to the same or related concepts. This creates a data interoperability bottleneck, as integrating data sets and knowledge bases that use identifiers for the same concepts from different resources requires such identifiers to be mapped to each other. However, available mappings are incomplete and fragmented across individual resources, motivating their large-scale integration.

Results: We developed SeMRA, a software tool that integrates mappings from multiple sources into a graph data structure. Using graph algorithms, it infers missing mappings implied by available ones while keeping track of provenance and confidence. This allows connecting identifier spaces between which direct mapping was previously not possible. SeMRA implements a customizable workflow that takes a declarative specification as input describing sources to integrate with additional configuration parameters. We used SeMRA to produce the SeMRA Raw Mappings Database, an aggregation of 43.4 million mappings from 127 sources that jointly cover identifiers from 445 ontologies and databases. We also describe benchmarks on specific use cases such as integrating mappings between resources cataloging diseases and cell types.

Availability: The code is available under the MIT license at https://github.com/biopragmatics/semra. The SeMRA Raw Mappings Database assembled by SeMRA is available at https://doi.org/10.5281/zenodo.11082038.

Supplementary information: Supplementary data are available at Bioinformatics online.

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