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A review for detecting gene-gene interactions using machine learning methods in genetic epidemiology.

Koo CL, Liew MJ, Mohamad MS, Salleh AH - Biomed Res Int (2013)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia.

ABSTRACT
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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Related in: MedlinePlus

Linear SVM with maximum-margin hyperplane.
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fig6: Linear SVM with maximum-margin hyperplane.

Mentions: In Figure 6, a hyperplane separates two classes of input that satisfying points x is given by w · x − b = 0 where w is the normal vector, b is the bias, and “·” is the dot product. According to [20], there are additional two hyperplane that separate the data from the previous hyperplane (defined by w · xi − b = 0) where there is no data between them. The additional hyperplane consists of maximum distance to the hyperplane (known as margin) that can prevent data points from falling into the margin and is defined by w · xi − b ≥ 1 for all xi that classified in first class and w · xi − b ≤ 1 for all xi that classified in second class.


A review for detecting gene-gene interactions using machine learning methods in genetic epidemiology.

Koo CL, Liew MJ, Mohamad MS, Salleh AH - Biomed Res Int (2013)

Linear SVM with maximum-margin hyperplane.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3818807&req=5

fig6: Linear SVM with maximum-margin hyperplane.
Mentions: In Figure 6, a hyperplane separates two classes of input that satisfying points x is given by w · x − b = 0 where w is the normal vector, b is the bias, and “·” is the dot product. According to [20], there are additional two hyperplane that separate the data from the previous hyperplane (defined by w · xi − b = 0) where there is no data between them. The additional hyperplane consists of maximum distance to the hyperplane (known as margin) that can prevent data points from falling into the margin and is defined by w · xi − b ≥ 1 for all xi that classified in first class and w · xi − b ≤ 1 for all xi that classified in second class.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus