Clinical and genomics data integration using meta-dimensional approach

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Subhani, Moeez, Anjum, Ashiq, Koop, Andreas and Antonopoulos, Nikolaos 2016. Clinical and genomics data integration using meta-dimensional approach. Association for Computing Machinery. https://doi.org/10.1145/2996890.3007896
AuthorsSubhani, Moeez, Anjum, Ashiq, Koop, Andreas and Antonopoulos, Nikolaos
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.

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.

KeywordsClinical data; Genomics data; Data integration; Bayesian networks; Neural networks
Year2016
JournalProceedings of the 9th International Conference on Utility and Cloud Computing
PublisherAssociation for Computing Machinery
Digital Object Identifier (DOI)https://doi.org/10.1145/2996890.3007896
Web address (URL)http://hdl.handle.net/10545/621409
hdl:10545/621409
ISBN9781450346160
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File Access Level
Open
Publication dates06 Dec 2016
Publication process dates
Deposited17 Feb 2017, 12:41
ContributorsDiagnostics Global, Informatics, F. Hoffmann-La Roche, Basel, Switzerland and University of Derby
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