Propagating semantic information in biochemical network models

BMC Bioinformatics

Methodology Article

Wolfram Liebermeister¹  Edda Klipp²  Marvin Schulz² 

[1] Institut für Biochemie, Charité-Universitätsmedizin Berlin, Seestr. 73, 13347, Berlin, Germany;Institut für Biologie, Theoretische Biophysik, Humboldt-Universität zu Berlin, Invalidenstr. 42, 10115, Berlin, Germany;

关键词: Feature Vector;    Model Element;    Semantic Information;    Similarity Propagation;    Semantic Annotation;   

DOI  :  10.1186/1471-2105-13-18

 received in 2011-11-02, accepted in 2012-01-30,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundTo enable automatic searches, alignments, and model combination, the elements of systems biology models need to be compared and matched across models. Elements can be identified by machine-readable biological annotations, but assigning such annotations and matching non-annotated elements is tedious work and calls for automation.ResultsA new method called "semantic propagation" allows the comparison of model elements based not only on their own annotations, but also on annotations of surrounding elements in the network. One may either propagate feature vectors, describing the annotations of individual elements, or quantitative similarities between elements from different models. Based on semantic propagation, we align partially annotated models and find annotations for non-annotated model elements.ConclusionsSemantic propagation and model alignment are included in the open-source library semanticSBML, available on sourceforge. Online services for model alignment and for annotation prediction can be used at http://www.semanticsbml.org.

【 授权许可】

CC BY   
© Schulz et al.; licensee BioMed Central Ltd. 2012