In this era of genome-wide association studies (GWAS), the quest for understanding the genetic architecture of complex diseases is rapidly increasing more than ever before. The development of high throughput genotyping and next generation sequencing technologies enables genetic epidemiological analysis of large scale data. These advances have led to the identification of a number of single nucleotide polymorphisms (SNPs) responsible for disease susceptibility. The interactions between SNPs associated with complex diseases are increasingly being explored in the current literature. These interaction studies are mathematically challenging and computationally complex. These challenges have been addressed by a number of data mining and machine learning approaches. This paper reviews the current methods and the related software packages to detect the SNP interactions that contribute to diseases. The issues that need to be considered when developing these models are addressed in this review. The paper also reviews the achievements in data simulation to evaluate the performance of these models. Further, it discusses the future of SNP interaction analysis.
Agent-oriented conceptual modelling notations such as i* have received considerable recent attention as a useful approach to early-phase requirements engineering. Agent-oriented conceptual modelling notations are highly effective in representing requirements from an intentional stance and answering questions such as what goals exist, how key actors depend on each other and what alternatives must be considered. Formal methods such as those based on the Z notation offer a complementary set of representational facilities. We explore how these two otherwise disparate approaches might be used in a synergistic fashion.
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