Literature DB >> 12066842

A database system for the analysis of biochemical pathways.

Isabel Rojas1, Luca Bernardi, Esther Ratsch, Renate Kania, Ulrike Wittig, Jasmin Saric.   

Abstract

To provide support for the analysis of biochemical pathways a database system based on a model that represents the characteristics of the domain is needed. This domain has proven to be difficult to model by using conventional data modelling techniques. We are building an ontology for biochemical pathways, which acts as the basis for the generation of a database on the same domain, allowing the definition of complex queries and complex data representation. The ontology is used as a modelling and analysis tool which allows the expression of complex semantics based on a first-order logic representation language. The induction capabilities of the system can help the scientist in formulating and testing research hypotheses that are difficult to express with the standard relational database mechanisms. An ontology representing the shared formalisation of the knowledge in a scientific domain can also be used as data integration tool clarifying the mapping of concepts to the developers of different databases. In this paper we describe the general structure of our system, concentrating on the ontology-based database as the key component of the system.

Mesh:

Year:  2002        PMID: 12066842

Source DB:  PubMed          Journal:  In Silico Biol        ISSN: 1386-6338


  4 in total

1.  Classification of chemical compounds to support complex queries in a pathway database.

Authors:  Ulrike Wittig; Andreas Weidemann; Renate Kania; Christian Peiss; Isabel Rojas
Journal:  Comp Funct Genomics       Date:  2004

2.  Developing a protein-interactions ontology.

Authors:  Esther Ratsch; Jörg Schultz; Jasmin Saric; Philipp Cimiano Lavin; Ulrike Wittig; Uwe Reyle; Isabel Rojas
Journal:  Comp Funct Genomics       Date:  2003

3.  Isolation and Characterization of a Bacterial Strain Capable of Efficient Berberine Degradation.

Authors:  Shiyue Liu; Yi Zhang; Ping Zeng; Heli Wang; Yonghui Song; Juan Li
Journal:  Int J Environ Res Public Health       Date:  2019-02-21       Impact factor: 3.390

4.  ARN: analysis and prediction by adipogenic professional database.

Authors:  Yan Huang; Li Wang; And Lin-Sen Zan
Journal:  BMC Syst Biol       Date:  2016-08-08
  4 in total

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