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    Clinical and genomics data integration using meta-dimensional approach

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    Authors
    Subhani, Moeez M.
    Anjum, Ashiq
    Koop, Andreas
    Antonopoulos, Nikolaos
    Issue Date
    2016-12-06
    
    Metadata
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    Abstract
    Clinical and genomics datasets contain humongous amount of information which are used in their respective environments independently to produce new science or better explain existing approaches. The interaction of data between these two domains is very limited and, hence, the information is disseminated. These disparate datasets need to be integrated to consolidate scattered pieces of information into a unified knowledge base to support new research challenges. However, there is no platform available that allows integration of clinical and genomics datasets into a consistent and coherent data source and produce analytics from it. We propose a data integration model here which will be capable of integrating clinical and genomics datasets using metadimensional approaches and machine learning methods. Bayesian Networks, which are based on meta-dimensional approach, will be used to design a probabilistic data model, and Neural Networks, which are based on machine learning, will be used for classification and pattern recognition from integrated data. This integration will help to coalesce the genetic background of clinical traits which will be immensely beneficial to derive new research insights for drug designing or precision medicine.
    Citation
    Subhani, M. M et al (2016) 'Clinical and genomics data integration using meta-dimensional approach', Proceedings of the 9th International Conference on Utility and Cloud Computing, New York : ACM, pp. 416-421
    Publisher
    Association for Computing Machinery
    Journal
    Proceedings of the 9th International Conference on Utility and Cloud Computing
    URI
    http://hdl.handle.net/10545/621409
    DOI
    10.1145/2996890.3007896
    Additional Links
    http://dl.acm.org/citation.cfm?doid=2996890.3007896
    Type
    Meetings and Proceedings
    Language
    en
    ISBN
    9781450346160
    ae974a485f413a2113503eed53cd6c53
    10.1145/2996890.3007896
    Scopus Count
    Collections
    Department of Electronics, Computing & Maths

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