Challenges in Integrating Biological Data Sources

Davidson, Susan B.; Overton, Gail; Buneman, Peter

doi:10.1089/cmb.1995.2.557

Cited by 139 publications

(80 citation statements)

References 26 publications

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“…Life Sciences data is characterized by its complexity, its high interrelatedness, its heterogeneity, and by a multitude of naming and identity issues [17], [18], [19]. Graph based models are a natural fit, as they are in many disciplines, to deal with these problems.…”

Section: A the Ondex Data Structure Life Sciences And The Semanticmentioning

confidence: 99%

Lost in Translation: Data Integration Tools Meet the Semantic Web (Experiences from the Ondex Project)

Splendiani

Rawlings

Kuo

et al. 2012

Lecture Notes in Electrical Engineering

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Abstract-More information is now being published in machine processable form on the web and, as de-facto distributed knowledge bases are materializing, partly encouraged by the vision of the Semantic Web, the focus is shifting from the publication of this information to its consumption. Platforms for data integration, visualization and analysis that are based on a graph representation of information appear first candidates to be consumers of web-based information that is readily expressible as graphs. The question is whether the adoption of these platforms to information available on the Semantic Web requires some adaptation of their data structures and semantics. Ondex is a network-based data integration, analysis and visualization platform which has been developed in a Life Sciences context. A number of features, including semantic annotation via ontologies and an attention to provenance and evidence, make this an ideal candidate to consume Semantic Web information, as well as a prototype for the application of network analysis tools in this context. By analyzing the Ondex data structure and its usage, we have found a set of discrepancies and errors arising from the semantic mismatch between a procedural approach to network analysis and the implications of a web-based representation of information. We report in the paper on the simple methodology that we have adopted to conduct such analysis, and on issues that we have found which may be relevant for a range of similar platforms.

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Section: A the Ondex Data Structure Life Sciences And The Semanticmentioning

confidence: 99%

Lost in Translation: Data Integration Tools Meet the Semantic Web (Experiences from the Ondex Project)

Splendiani

Rawlings

Kuo

et al. 2012

Lecture Notes in Electrical Engineering

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“…Among these challenges, data complexity is the most challenging problem in our context. Biological data are complex because they are heterogeneous [7], incomplete, uncertain and inconsistent [31]. Despite these characteristics, the expertise of proteomic experiments requires high data quality to make pertinent conclusions.…”

Section: Problems Related To the Use Of Datamentioning

confidence: 99%

A Approach to Clinical Proteomics Data Quality Control and Import

Naubourg

Savonnet

Leclercq

et al. 2011

Information Technology in Bio- And Medical Informatics

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Abstract. Biomedical domain and proteomics in particular are faced with an increasing volume of data. The heterogeneity of data sources implies heterogeneity in the representation and in the content of data. Data may also be incorrect, implicate errors and can compromise the analysis of experiments results. Our approach aims to ensure the initial quality of data during import into an information system dedicated to proteomics. It is based on the joint use of models, which represent the system sources, and ontologies, which are use as mediators between them. The controls, we propose, ensure the validity of values, semantics and data consistency during import process.

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“…Data integration in the life science is an ongoing challenge in Bioinformatics; problems arise because standards for data formats, identifiers, common vocabularies and agreed semantics between databases are lacking [5,6]. Data in the life sciences are complex and volatile that, when taken with the issues outlined, makes the necessary integration of life sciences data hard work.…”

Section: Introductionmentioning

confidence: 99%

Developing a kidney and urinary pathway knowledge base

et al. 2011

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BackgroundChronic renal disease is a global health problem. The identification of suitable biomarkers could facilitate early detection and diagnosis and allow better understanding of the underlying pathology. One of the challenges in meeting this goal is the necessary integration of experimental results from multiple biological levels for further analysis by data mining. Data integration in the life science is still a struggle, and many groups are looking to the benefits promised by the Semantic Web for data integration.ResultsWe present a Semantic Web approach to developing a knowledge base that integrates data from high-throughput experiments on kidney and urine. A specialised KUP ontology is used to tie the various layers together, whilst background knowledge from external databases is incorporated by conversion into RDF. Using SPARQL as a query mechanism, we are able to query for proteins expressed in urine and place these back into the context of genes expressed in regions of the kidney.ConclusionsThe KUPKB gives KUP biologists the means to ask queries across many resources in order to aggregate knowledge that is necessary for answering biological questions. The Semantic Web technologies we use, together with the background knowledge from the domain’s ontologies, allows both rapid conversion and integration of this knowledge base. The KUPKB is still relatively small, but questions remain about scalability, maintenance and availability of the knowledge itself.AvailabilityThe KUPKB may be accessed via http://www.e-lico.eu/kupkb.

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Challenges in Integrating Biological Data Sources

Cited by 139 publications

References 26 publications

Lost in Translation: Data Integration Tools Meet the Semantic Web (Experiences from the Ondex Project)

Lost in Translation: Data Integration Tools Meet the Semantic Web (Experiences from the Ondex Project)

A Approach to Clinical Proteomics Data Quality Control and Import

Developing a kidney and urinary pathway knowledge base

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