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edge about genetic variants that influence the suscep-
tibility to complex diseases. In the process, it may
help gain progress across multiple-domains, includ-
ing:
• machine learning and data mining: statistical
models and techniques for dealing with GWAS
data that take account high-dimensionality and
variable correlation will be designed; methods for
the improvement of models through integrating
additional knowledge will be proposed;
• medicine: the methods designed intend to bring
new evidence of genetic disease susceptibility
as well as individual genetic susceptibility to
drugs, thus offering perspectives for personalized
medicine;
• public health: our work will contribute to account-
ing for the societal evolution trend toward early
gene susceptibility detection to improve preven-
tion or surveillance;
• economy: The animal and plant biology domains
are also concerned with respect to the selection of
phenotypes of interest in agronomy as well;
• high-performance computing for GWASs: cutting
edge techniques for implementing GWAS strate-
gies and large-scale machine learning approaches
will be implemented.
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