Seattle Children's Research Institute, Center for Global Infectious Disease Research, Seattle, WA, USA.
Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA.
Bioinformatics. 2019 May 1;35(9):1600-1602. doi: 10.1093/bioinformatics/bty829.
As the number and complexity of biosimulation models grows, so do demands for tools that can help users understand models and compose more comprehensive and accurate systems from existing models. SemGen is a tool for semantics-based annotation and composition of biosimulation models designed to address this demand. A key SemGen capability is to decompose and then integrate models across existing model exchange formats including SBML and CellML. To support this capability, we use semantic annotations to explicitly capture the underlying biological and physical meanings of the entities and processes that are modeled. SemGen leverages annotations to expose a model's biological and computational architecture and to help automate model composition.
SemGen is freely available at https://github.com/SemBioProcess/SemGen.
Supplementary data are available at Bioinformatics online.
随着生物仿真模型数量和复杂度的增加，用户对于能够帮助他们理解模型并从现有模型中构建更全面、更准确系统的工具的需求也在不断增加。SemGen 是一个基于语义的注释和生物仿真模型组合工具，旨在满足这一需求。SemGen 的一个关键功能是分解并集成现有的模型交换格式（包括 SBML 和 CellML）中的模型。为了支持这一功能，我们使用语义注释来显式捕获所建模的实体和过程的潜在生物学和物理意义。SemGen 利用注释来揭示模型的生物学和计算架构，并帮助实现模型的自动组合。
SemGen 可在 https://github.com/SemBioProcess/SemGen 上免费获取。
补充数据可在 Bioinformatics 在线获取。
