Please use this identifier to cite or link to this item:
Title: PIBAS FedSPARQL: A web-based platform for integration and exploration of bioinformatics datasets
Authors: Djokic-Petrovic M.
Cvjetković, Vladimir
Yang J.
Živanovic M.
Wild D.
Journal: Journal of Biomedical Semantics
Issue Date: 20-Sep-2017
Abstract: © 2017 The Author(s). Background: There are a huge variety of data sources relevant to chemical, biological and pharmacological research, but these data sources are highly siloed and cannot be queried together in a straightforward way. Semantic technologies offer the ability to create links and mappings across datasets and manage them as a single, linked network so that searching can be carried out across datasets, independently of the source. We have developed an application called PIBAS FedSPARQL that uses semantic technologies to allow researchers to carry out such searching across a vast array of data sources. Results: PIBAS FedSPARQL is a web-based query builder and result set visualizer of bioinformatics data. As an advanced feature, our system can detect similar data items identified by different Uniform Resource Identifiers (URIs), using a text-mining algorithm based on the processing of named entities to be used in Vector Space Model and Cosine Similarity Measures. According to our knowledge, PIBAS FedSPARQL was unique among the systems that we found in that it allows detecting of similar data items. As a query builder, our system allows researchers to intuitively construct and run Federated SPARQL queries across multiple data sources, including global initiatives, such as Bio2RDF, Chem2Bio2RDF, EMBL-EBI, and one local initiative called CPCTAS, as well as additional user-specified data source. From the input topic, subtopic, template and keyword, a corresponding initial Federated SPARQL query is created and executed. Based on the data obtained, end users have the ability to choose the most appropriate data sources in their area of interest and exploit their Resource Description Framework (RDF) structure, which allows users to select certain properties of data to enhance query results. Conclusions: The developed system is flexible and allows intuitive creation and execution of queries for an extensive range of bioinformatics topics. Also, the novel similar data items detection algorithm can be particularly useful for suggesting new data sources and cost optimization for new experiments. PIBAS FedSPARQL can be expanded with new topics, subtopics and templates on demand, rendering information retrieval more robust.
Type: Article
DOI: 10.1186/s13326-017-0151-z
SCOPUS: 85029772636
Appears in Collections:Faculty of Science, Kragujevac
[ Google Scholar ]

Page views(s)




Files in This Item:
File Description SizeFormat 
10.1186-s13326-017-0151-z.pdf4.76 MBAdobe PDFThumbnail

Items in SCIDAR are protected by copyright, with all rights reserved, unless otherwise indicated.