Disease susceptibility prediction is defined as follows. Given training set S and a test case t∉S as a tuple (known as SNP, unknown disease), trying predicting the unknown disease with maximum accuracy. DisGeNET is a proponent dataset in disease susceptibility research. This paper reviews DisGeNET comprehensive information, before introducing a proposed system operating atop it. First, vetting the dataset by consolidation, and removing genes with effects beyond a certain threshold. Second, computing the empirical cumulative distribution function, using it for plotting and printing gene associations for many diseases such as, and not limited to, Alzheimer, Anemia, and Brain, breast cancer proposed methods such as applying C4.5 & naïve Bayes give better accuracy then previous works
Abdelbadeea, E. E., El-Dosuky, M. A., & Rashad, M. Z. (2020). Gene-Disease Association. Mansoura Journal for Computer and Information Sciences, 16(2), 1-9. doi: 10.21608/mjcis.2020.321071
MLA
E. E. Abdelbadeea; M. A. El-Dosuky; M. Z. Rashad. "Gene-Disease Association", Mansoura Journal for Computer and Information Sciences, 16, 2, 2020, 1-9. doi: 10.21608/mjcis.2020.321071
HARVARD
Abdelbadeea, E. E., El-Dosuky, M. A., Rashad, M. Z. (2020). 'Gene-Disease Association', Mansoura Journal for Computer and Information Sciences, 16(2), pp. 1-9. doi: 10.21608/mjcis.2020.321071
VANCOUVER
Abdelbadeea, E. E., El-Dosuky, M. A., Rashad, M. Z. Gene-Disease Association. Mansoura Journal for Computer and Information Sciences, 2020; 16(2): 1-9. doi: 10.21608/mjcis.2020.321071
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