Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs), support vector machine (SVM), and random forests (RFs) in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.

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Recently, metabolic engineering has gained popularity in the area of system biology due to its potential of capable to increase production. It works by manipulating the genes that can restructure the metabolic networks that have the potential to increase the yield of metabolite production. This metabolic network model has become the foundation for the development of computational procedures to suggest genetic manipulations that eventually leads to optimizing the metabolite production. This research focuses on optimizing the metabolite production of succinate in Escherichia coli. Previous works on rational modelling framework failed in optimizing the metabolite production and tended to suggest unrealistic flux distributions. Hence, in this paper, a hybrid algorithm of Bees Algorithm and Minimization of Metabolic Adjustment (BAMOMA) is proposed to overcome the problems found in the previous works. By developing this hybrid algorithm, it helps to identify a set of gene in Escherichia coli dataset that can be deleted and eventually leads to overproduction of succinate. Experimental results show that BAMOMA is better than previous works in term of production rates.