Ranked retrieval of Computational Biology models

BMC Bioinformatics

Methodology Article

Lukas Endler¹  Nicolas Le Novère¹  Andre Peters²  Dagmar Waltemath²  Ron Henkel³ 

[1] Computational Neurobiology, European Bioinformatics Institute, Hinxton, UK;Database and Information Systems, University of Rostock, Rostock, Germany;Database and Information Systems, University of Rostock, Rostock, Germany;Computational Neurobiology, European Bioinformatics Institute, Hinxton, UK;

关键词: Caffeine;    Ranking Function;    Model Index;    Vector Space Model;    Information Retrieval Technique;   

DOI  :  10.1186/1471-2105-11-423

 received in 2010-05-12, accepted in 2010-08-11,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundThe 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.ResultsHere 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.ConclusionsThe 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.

【 授权许可】

Unknown   
© Henkel et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.