Ranked retrieval of Computational Biology models

Background: 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. Results: 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 Dat...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Ron Henkel, Lukas Endler, Andre Peters, Nicolas Le Novère, Dagmar Waltemath
• Keywords: Biological System, BMCBI 2010, Computational Models, Model