A Semantic Web Management Model for Integrative Biomedical Informatics

Carmen Behrens,Jonas S. Almeida,Lynn H. Vogel,Allen Chang,Harold R. Garner,Arlene M. Correa,Helena F. Deus,Diogo F. Veiga,Eshel Ben-Jacob,Romesh Stanislaus,John D. Minna,Bradley Broom,Ignacio I. Wistuba,Kevin Coombes,Stephen G. Swisher,Jack A. Roth

doi:10.1371/journal.pone.0002946

Abstract

BackgroundData, data everywhere. The diversity and magnitude of the data generated in the Life Sciences defies automated articulation among complementary efforts. The additional need in this field for managing property and access permissions compounds the difficulty very significantly. This is particularly the case when the integration involves multiple domains and disciplines, even more so when it includes clinical and high throughput molecular data.Methodology/Principal FindingsThe emergence of Semantic Web technologies brings the promise of meaningful interoperation between data and analysis resources. In this report we identify a core model for biomedical Knowledge Engineering applications and demonstrate how this new technology can be used to weave a management model where multiple intertwined data structures can be hosted and managed by multiple authorities in a distributed management infrastructure. Specifically, the demonstration is performed by linking data sources associated with the Lung Cancer SPORE awarded to The University of Texas MDAnderson Cancer Center at Houston and the Southwestern Medical Center at Dallas. A software prototype, available with open source at www.s3db.org, was developed and its proposed design has been made publicly available as an open source instrument for shared, distributed data management.Conclusions/SignificanceThe Semantic Web technologies have the potential to addresses the need for distributed and evolvable representations that are critical for systems Biology and translational biomedical research. As this technology is incorporated into application development we can expect that both general purpose productivity software and domain specific software installed on our personal computers will become increasingly integrated with the relevant remote resources. In this scenario, the acquisition of a new dataset should automatically trigger the delegation of its analysis.

Highlights

Data management and analysis for the life sciences ‘‘The laws of Nature are written in the language of mathematics’’ famously said Galileo
Conclusions/Significance: The Semantic Web technologies have the potential to addresses the need for distributed and evolvable representations that are critical for systems Biology and translational biomedical research
As this technology is incorporated into application development we can expect that both general purpose productivity software and domain specific software installed on our personal computers will become increasingly integrated with the relevant remote resources

Summary

Introduction

Data management and analysis for the life sciences ‘‘The laws of Nature are written in the language of mathematics’’ famously said Galileo. In recent years efforts to analyze the increasing amount and diversity of data in the Life Sciences has been correspondingly constrained not so much by our ability to read it as by the challenge of organizing it The urgency of this task and the reward of even partial success in its accomplishment have caused the interoperability between diverse digital representations to take center stage [1,2,3,4,5]. For those in the Life Sciences enticed by Galileo’s pronouncement, the effort of collecting data is no longer focused solely on field/ bench work. This is the case when the integration involves multiple domains and disciplines, even more so when it includes clinical and high throughput molecular data

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