Abstract
High-throughput single nucleotide polymorphism (SNP) genotyping technologies make massive genotype data, with a large number of individuals, publicly available. Accessibility of genetic data makes genome-wide association studies for complex diseases possible. One of the most challenging issues in genome-wide association studies is to search and analyze genetic risk factors resulting from interactions of multiple genes. The integrated risk factor usually have a higher risk rate than single SNPs. This paper explores the possibility of applying random forest to search disease-associated factors for given case/control samples. An optimum random forest based algorithm is proposed for the disease susceptibility prediction problem. The proposed method has been applied to publicly available genotype data on Crohn’s disease and autoimmune disorders for predicting susceptibility to these diseases. The achieved accuracy of prediction is higher than those achieved by universal prediction methods such as Support Vector Machine (SVM) and previous known methods.
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Mao, W., Kelly, S. (2007). An Optimum Random Forest Model for Prediction of Genetic Susceptibility to Complex Diseases. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_21
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DOI: https://doi.org/10.1007/978-3-540-71701-0_21
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