Abstract
Single nucleotide polymorphisms (SNPs), as one kind of the most common genetic variations, are responsible for individual differences. Furthermore, SNPs are found to be closely associated with many major kinds of diseases that could affect human health, such as hypertension, diabetes, cancer and mental illness. To accurately distinguish functionally related variants from the mass background genetic variations is a significant challenge facing biology and computer scientists and the challenge becomes more severe when dealing with variants in non-coding human genome. In this study, we present a deep belief networks (DBNs) based prediction method to identify candidate disease-associated non-coding SNPs in human genome. For feature extraction, we propose a digital coding based method to convent the nucleotide sequences of SNPs into numerical vectors directly as the input of DBNs. Then the DBNs with 10 layers are used to build the prediction model. 10-fold cross-validation result shows that the proposed method can achieve accuracy with the sensitivity of 73.48% and specificity of 74.31%. Since there is no any artificial feature needed, our approach can get rid of the dependence on huge amounts of genome annotation data which used by other traditional methods.
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Acknowledgment
We are grateful to the National Engineering Laboratory for Logistics Information Technology, YuanTong Express co. LTD.
Funding
This work was supported by grants from the Scientific Research Foundation of Nanjing University of Posts and Telecommunications (No. NY218143).
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Li, R., Xiang, F., Wu, F., Sun, Z. (2019). A Deep Belief Networks Based Prediction Method for Identification of Disease-Associated Non-coding SNPs in Human Genome. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11633. Springer, Cham. https://doi.org/10.1007/978-3-030-24265-7_2
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DOI: https://doi.org/10.1007/978-3-030-24265-7_2
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