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[Regular Paper] An Intensive Search for Higher-Order Gene-Gene Interactions by Improving Deep Learning Model
- Source :
- 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE).
- Publication Year :
- 2018
- Publisher :
- IEEE, 2018.
-
Abstract
- In the new era of genetic epidemiology, there have been growing interest in studying genetic variants and their associations to complex diseases. Advances in modern computational approaches have led to the search for useful interacting genetic variants that are associated to the manifestation of a disease. However, these conventional strategies face number of challenges in predicting interesting interactions when data acquisition and dimensionality increases. Deep learning promises empirical success in number of applications including bioinformatics to drive insights of biological complexities. A deep neural network was previously proposed to identify true causative two-locus SNP interactions. The method was evaluated on various simulated and real datasets. In this study, the performance of the previously proposed deep learning method is maximized by improving network learning and avoiding overfitting. The method is further extended for performing sensitivity analysis. The performance of the method is evaluated on chronical dialysis patient's data for identifying higher-order interactions. It was observed that the highly ranked two-locus and three-locus SNP interactions in mitochondrial D-loop has the highest risk for the manifestation of disease.
- Subjects :
- 0301 basic medicine
Artificial neural network
business.industry
Computer science
Deep learning
0206 medical engineering
02 engineering and technology
Overfitting
Machine learning
computer.software_genre
03 medical and health sciences
030104 developmental biology
Order (biology)
Genetic epidemiology
Epistasis
SNP
Artificial intelligence
business
computer
020602 bioinformatics
Curse of dimensionality
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)
- Accession number :
- edsair.doi...........025792426df45ef0a88a221bf402ca73