Ranked retrieval of Computational Biology models.
Ranked retrieval of Computational Biology models.
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DOI：
10.1186/1471-2105-11-423
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发表时间：
2010-08-11
影响因子：
3
通讯作者：
Waltemath D
中科院分区：
文献类型：
--
作者：
Henkel R;Endler L;Peters A;Le Novère N;Waltemath D
生物系统的研究需要计算支持。如果针对一个生物学问题，重复使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不断增加，选择可能合适的模型变得更具挑战性，当考虑到模型日益复杂时更是如此。首先，在一组潜在的模型候选中，很难确定最符合自身需求的模型。其次，很难把握搜索结果集中列出的未知模型的性质，也难以判断它与自己心中特定问题的契合程度。
在此，我们提出一种针对生物过程计算模型的改进搜索方法。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建议的注释以及其他元信息。它现在是BioModels数据库搜索引擎的一部分，BioModels数据库是计算模型的标准存储库。
据我们所知，所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。以BioModels数据库为例，结果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。我们的系统相对于现有解决方案的优势在于我们纳入了丰富的元信息集，并且为用户提供了针对一个查询所找到的模型的相关性排名。模型数据库中更好的搜索能力有望对现有模型的重复使用产生积极影响。
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.