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Open Access Highly Accessed Research article

S3DB core: a framework for RDF generation and management in bioinformatics infrastructures

Jonas S Almeida1*, Helena F Deus12 and Wolfgang Maass3

Author Affiliations

1 Department of Bioinformatics and Computational Biology, The University of Texas M D Anderson Cancer Center, 1515 Holcombe Blvd Houston, TX 77030, USA

2 Institute of Chemical and Biological Technology, Universidade Nova de Lisboa, Oeiras, Portugal

3 Research Center for Intelligent Media, Furtwangen University, Furtwangen, Germany

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BMC Bioinformatics 2010, 11:387  doi:10.1186/1471-2105-11-387

Published: 20 July 2010



Biomedical research is set to greatly benefit from the use of semantic web technologies in the design of computational infrastructure. However, beyond well defined research initiatives, substantial issues of data heterogeneity, source distribution, and privacy currently stand in the way towards the personalization of Medicine.


A computational framework for bioinformatic infrastructure was designed to deal with the heterogeneous data sources and the sensitive mixture of public and private data that characterizes the biomedical domain. This framework consists of a logical model build with semantic web tools, coupled with a Markov process that propagates user operator states. An accompanying open source prototype was developed to meet a series of applications that range from collaborative multi-institution data acquisition efforts to data analysis applications that need to quickly traverse complex data structures. This report describes the two abstractions underlying the S3DB-based infrastructure, logical and numerical, and discusses its generality beyond the immediate confines of existing implementations.


The emergence of the "web as a computer" requires a formal model for the different functionalities involved in reading and writing to it. The S3DB core model proposed was found to address the design criteria of biomedical computational infrastructure, such as those supporting large scale multi-investigator research, clinical trials, and molecular epidemiology.