Developing a kidney and urinary pathway knowledge base View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2011-05-17

AUTHORS

Simon Jupp, Julie Klein, Joost Schanstra, Robert Stevens

ABSTRACT

BACKGROUND: Chronic 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. RESULTS: We 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. CONCLUSIONS: The 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. AVAILABILITY: The KUPKB may be accessed via http://www.e-lico.eu/kupkb. More... »

PAGES

s7-s7

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    URI

    http://scigraph.springernature.com/pub.10.1186/2041-1480-2-s2-s7

    DOI

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    DIMENSIONS

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    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/21624162


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    33 schema:description BACKGROUND: Chronic 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. RESULTS: We 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. CONCLUSIONS: The 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. AVAILABILITY: The KUPKB may be accessed via http://www.e-lico.eu/kupkb.
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    41 KUP ontology
    42 KUPKB
    43 Kup
    44 LiCo
    45 RDF
    46 SPARQL
    47 Semantic Web
    48 Semantic Web approach
    49 Semantic Web technologies
    50 Web
    51 Web technologies
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    57 benefits
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    59 biological levels
    60 biological questions
    61 biomarkers
    62 challenges
    63 chronic renal disease
    64 context
    65 context of genes
    66 conversion
    67 data
    68 data integration
    69 data mining
    70 database
    71 detection
    72 diagnosis
    73 disease
    74 domain ontology
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    76 experimental results
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    79 genes
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    85 identification
    86 integration
    87 kidney
    88 knowledge
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    92 life sciences
    93 maintenance
    94 means
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    96 mining
    97 multiple biological levels
    98 necessary integration
    99 ontology
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    102 pathway knowledge base
    103 problem
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    110 renal disease
    111 resources
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