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Ontology-based instance data validation for high-quality curated biological pathways.

Jeong E, Nagasaki M, Ueno K, Miyano S - BMC Bioinformatics (2011)

Bottom Line: The experimental results show that the relatively simple rules can efficiently detect errors made during curation, such as misassignment and misuse of ontology concepts and terms in curated models.Our rule-based validation embedding biological semantics enables us to provide high-quality curated biological pathways.This approach can serve as a preprocessing step for model integration, exchange and extraction data, and simulation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo 108-8639, Japan. eajeong@ims.u-tokyo.ac.jp

ABSTRACT

Background: Modeling in systems biology is vital for understanding the complexity of biological systems across scales and predicting system-level behaviors. To obtain high-quality pathway databases, it is essential to improve the efficiency of model validation and model update based on appropriate feedback.

Results: We have developed a new method to guide creating novel high-quality biological pathways, using a rule-based validation. Rules are defined to correct models against biological semantics and improve models for dynamic simulation. In this work, we have defined 40 rules which constrain event-specific participants and the related features and adding missing processes based on biological events. This approach is applied to data in Cell System Ontology which is a comprehensive ontology that represents complex biological pathways with dynamics and visualization. The experimental results show that the relatively simple rules can efficiently detect errors made during curation, such as misassignment and misuse of ontology concepts and terms in curated models.

Conclusions: A new rule-based approach has been developed to facilitate model validation and model complementation. Our rule-based validation embedding biological semantics enables us to provide high-quality curated biological pathways. This approach can serve as a preprocessing step for model integration, exchange and extraction data, and simulation.

Show MeSH
ME UnknownActivation violating cardinality constraint. Legend: A and B represent the original model and the corrected model after validation, respectively. The biological event causing warnings and the modified parts are in red boxes in the images. The same legend is used in Figure 4. From the literature, we found that Ras{active} acts as an activator of the process, not as an inputprocess entity. Then, the connector from Ras{active} to the process is changed to a dashed line with an arrow in B.
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Figure 3: ME UnknownActivation violating cardinality constraint. Legend: A and B represent the original model and the corrected model after validation, respectively. The biological event causing warnings and the modified parts are in red boxes in the images. The same legend is used in Figure 4. From the literature, we found that Ras{active} acts as an activator of the process, not as an inputprocess entity. Then, the connector from Ras{active} to the process is changed to a dashed line with an arrow in B.

Mentions: We checked each model based on the warnings related to the cardinality constraint and corrected each model by reviewing the literature used to generate the model. Two cases are selected to show how our validation approach facilitates to correct the macrophage models. In Figures 3 and 4, A and B indicate the original model and the corrected model after validation, respectively. The red boxes in the figures reveal the places in which the problem happened and the model is changed.


Ontology-based instance data validation for high-quality curated biological pathways.

Jeong E, Nagasaki M, Ueno K, Miyano S - BMC Bioinformatics (2011)

ME UnknownActivation violating cardinality constraint. Legend: A and B represent the original model and the corrected model after validation, respectively. The biological event causing warnings and the modified parts are in red boxes in the images. The same legend is used in Figure 4. From the literature, we found that Ras{active} acts as an activator of the process, not as an inputprocess entity. Then, the connector from Ras{active} to the process is changed to a dashed line with an arrow in B.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3044316&req=5

Figure 3: ME UnknownActivation violating cardinality constraint. Legend: A and B represent the original model and the corrected model after validation, respectively. The biological event causing warnings and the modified parts are in red boxes in the images. The same legend is used in Figure 4. From the literature, we found that Ras{active} acts as an activator of the process, not as an inputprocess entity. Then, the connector from Ras{active} to the process is changed to a dashed line with an arrow in B.
Mentions: We checked each model based on the warnings related to the cardinality constraint and corrected each model by reviewing the literature used to generate the model. Two cases are selected to show how our validation approach facilitates to correct the macrophage models. In Figures 3 and 4, A and B indicate the original model and the corrected model after validation, respectively. The red boxes in the figures reveal the places in which the problem happened and the model is changed.

Bottom Line: The experimental results show that the relatively simple rules can efficiently detect errors made during curation, such as misassignment and misuse of ontology concepts and terms in curated models.Our rule-based validation embedding biological semantics enables us to provide high-quality curated biological pathways.This approach can serve as a preprocessing step for model integration, exchange and extraction data, and simulation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo 108-8639, Japan. eajeong@ims.u-tokyo.ac.jp

ABSTRACT

Background: Modeling in systems biology is vital for understanding the complexity of biological systems across scales and predicting system-level behaviors. To obtain high-quality pathway databases, it is essential to improve the efficiency of model validation and model update based on appropriate feedback.

Results: We have developed a new method to guide creating novel high-quality biological pathways, using a rule-based validation. Rules are defined to correct models against biological semantics and improve models for dynamic simulation. In this work, we have defined 40 rules which constrain event-specific participants and the related features and adding missing processes based on biological events. This approach is applied to data in Cell System Ontology which is a comprehensive ontology that represents complex biological pathways with dynamics and visualization. The experimental results show that the relatively simple rules can efficiently detect errors made during curation, such as misassignment and misuse of ontology concepts and terms in curated models.

Conclusions: A new rule-based approach has been developed to facilitate model validation and model complementation. Our rule-based validation embedding biological semantics enables us to provide high-quality curated biological pathways. This approach can serve as a preprocessing step for model integration, exchange and extraction data, and simulation.

Show MeSH