Ranked retrieval of Computational Biology models

<p>Abstract</p> <p>Background</p> <p>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 ch...

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Main Authors: Henkel Ron, Endler Lukas, Peters Andre, Le Novère Nicolas, Waltemath Dagmar
Format: Article
Language:English
Published: BMC 2010-08-01
Series:BMC Bioinformatics
Online Access:http://www.biomedcentral.com/1471-2105/11/423
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spelling doaj-b25cbdd38be147feb444222235ffadfc2020-11-25T00:18:54ZengBMCBMC Bioinformatics1471-21052010-08-0111142310.1186/1471-2105-11-423Ranked retrieval of Computational Biology modelsHenkel RonEndler LukasPeters AndreLe Novère NicolasWaltemath Dagmar<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p> http://www.biomedcentral.com/1471-2105/11/423
collection DOAJ
language English
format Article
sources DOAJ
author Henkel Ron
Endler Lukas
Peters Andre
Le Novère Nicolas
Waltemath Dagmar
spellingShingle Henkel Ron
Endler Lukas
Peters Andre
Le Novère Nicolas
Waltemath Dagmar
Ranked retrieval of Computational Biology models
BMC Bioinformatics
author_facet Henkel Ron
Endler Lukas
Peters Andre
Le Novère Nicolas
Waltemath Dagmar
author_sort Henkel Ron
title Ranked retrieval of Computational Biology models
title_short Ranked retrieval of Computational Biology models
title_full Ranked retrieval of Computational Biology models
title_fullStr Ranked retrieval of Computational Biology models
title_full_unstemmed Ranked retrieval of Computational Biology models
title_sort ranked retrieval of computational biology models
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2010-08-01
description <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p>
url http://www.biomedcentral.com/1471-2105/11/423
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