Database and Information Systems, University of Rostock, Rostock, Germany.
BMC Bioinformatics. 2010 Aug 11;11:423. doi: 10.1186/1471-2105-11-423.
The study of biological systems demands computational support. If targeting a biological problem, the reuse of existing computational models can save time and effort. Deciding for potentially suitable models, however, becomes more challenging with the increasing number of computational models available, and even more when considering the models' growing complexity. Firstly, among a set of potential model candidates it is difficult to decide for the model that best suits ones needs. Secondly, it is hard to grasp the nature of an unknown model listed in a search result set, and to judge how well it fits for the particular problem one has in mind.
Here we present an improved search approach for computational models of biological processes. It is based on existing retrieval and ranking methods from Information Retrieval. The approach incorporates annotations suggested by MIRIAM, and additional meta-information. It is now part of the search engine of BioModels Database, a standard repository for computational models.
The introduced concept and implementation are, to our knowledge, the first application of Information Retrieval techniques on model search in Computational Systems Biology. Using the example of BioModels Database, it was shown that the approach is feasible and extends the current possibilities to search for relevant models. The advantages of our system over existing solutions are that we incorporate a rich set of meta-information, and that we provide the user with a relevance ranking of the models found for a query. Better search capabilities in model databases are expected to have a positive effect on the reuse of existing models.
生物系统的研究需要计算支持。如果针对一个生物学问题，那么重用现有的计算模型可以节省时间和精力。然而，随着可用的计算模型数量的增加，选择潜在合适的模型变得更加具有挑战性，而当考虑到模型的日益复杂性时，情况则更加复杂。首先，在一组潜在的模型候选者中，很难决定哪个模型最适合需求。其次，很难理解搜索结果集中列出的未知模型的本质，也很难判断它与脑海中特定问题的契合程度。
本文提出了一种改进的生物过程计算模型搜索方法。它基于信息检索中现有的检索和排序方法。该方法结合了 MIRIAM 提出的注释和其他元信息。它现在是生物模型数据库搜索引擎的一部分，生物模型数据库是计算模型的标准存储库。
据我们所知，引入的概念和实现是信息检索技术在计算系统生物学模型搜索中的首次应用。通过生物模型数据库的示例，表明该方法是可行的，并扩展了当前搜索相关模型的可能性。与现有解决方案相比，我们系统的优势在于我们整合了丰富的元信息，并为用户提供了查询结果模型的相关性排名。更好的模型数据库搜索功能有望对现有模型的重用产生积极影响。
