Optimized seamless integration of biomolecular data

Eckman, Barbara; Lacroix, Zoé; Raschid, Louiqa

doi:10.1109/bibe.2001.974408

Cited by 28 publications

(13 citation statements)

References 53 publications

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“…DiscoveryLink [14] uses the relational model and SQL language for providing access to wrapped data sources and includes query planning and optimization capabilities. Eckman et al [10] present a similar system with a comparison to many existing related efforts. BioKleisli [7] adopts a nested relational model, extends SQL, and provides access to complex sources with structured data.…”

Section: Related Workmentioning

confidence: 93%

Automatic Discovery and Inferencing of Complex Bioinformatics Web Interfaces

et al. 2005

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The World Wide Web provides a vast resource to genomics researchers, with Web-based access to distributed data sources such as BLAST sequence homology search interfaces. However, finding the desired scientific information can still be very tedious and frustrating. While there are several known servers on genomic data (e.g., GeneBank, EMBL, NCBI) that are shared and accessed frequently, new data sources are created each day in laboratories all over the world. Sharing these new genomics results is hindered by the lack of a common interface or data exchange mechanism. Moreover, the number of autonomous genomics sources and their rate of change outpace the speed at which they can be manually identified, meaning that the available data is not being utilized to its full potential. An automated system that can find, classify, describe, and wrap new sources without tedious and low-level coding of source-specific wrappers is needed to assist scientists in accessing hundreds of dynamically changing bioinformatics Web data sources through a single interface. A correct classification of any kind of Web data source must address both the capability of the source and the conversation/interaction semantics inherent in the design of the data source. We propose a service class description (SCD)-a meta-data approach for classifying Web data sources that takes into account both the capability and the conversational semantics of the source. The ability to discover the interaction pattern of a Web source leads to increased accuracy in the classification process. Our results show that an SCD-based approach successfully classifies two thirds of BLAST sites with 100% accuracy and two thirds of bioinformatics keyword search sites with around 80% precision.

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Section: Related Workmentioning

confidence: 93%

Automatic Discovery and Inferencing of Complex Bioinformatics Web Interfaces

et al. 2005

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“…The problem of managing this information is not solved satisfactorily, although it has been recognized as a key component of today's genome/biology research. [5] …”

Section: Bioinformatic Data 21 Management Of Bioinformatics Datamentioning

confidence: 99%

Analysis and Design of Bio-Info-Collaborator Service Model to Collaborate Multiple Heterogeneous Bioinformatics Web Services

Soni¹,

Nain²

2012

IJCA

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In this paper, an approach to generate a mechanism for bioinformatics web services to register them with a Data Collaborator Web Service has been presented. The aim of this approach is to create new collaborator service model using existing web services. Up to now, the focus has been on answering specific user queries. On the contrary, the plan is to focus on an automated mechanism that can work as a data collaborator web service. This mechanism would be able to respond to a range of inputs to provide desired and efficient output that are required by the service user. General TermsExistence of several large bioinformatics databases on Global Internet resulted the need for an approach to collaborate information from multiple data sources and services. In recent years, technology has moved so fast to develop major advances to access data on the web. There was a time when data access was limited to text files and that was accessible to a limited number of people. Now technology gave us so much huge and powerful resources like online data systems capable to store several terabytes of data.

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“…The union of two parts of PO, O1 = (N1, E1, R1), and O2 = (N2, E2, R2) is expressed as: (1,2), and RU = R1 ∪ R2 ∪ RI (1,2), where, OI (1, 2) = O1 ∩ SR O2 = (NI (1,2), EI (1,2), RI (1,2)) is the intersection of two ontologies. The union operation combines two parts of the ontology retaining only one copy of the concepts in the intersection.…”

Section: Union Operatormentioning

confidence: 99%

“…Even without a unified approach to biological information management as proposed in earlier works [1][2][3], one may still note that a huge wealth of information is available publicly. Despite data being universally accessible for any given research area, what distinguishes a novice from an expert is the knowledge of what information is relevant.…”

Section: Introductionmentioning

confidence: 99%