碩士 / 國立臺灣大學 / 生醫電子與資訊學研究所 / 106 / Alzheimer''s disease (AD) is a condition that worsens over time, and symptoms can gradually deteriorate from memory loss to loss of mobility and ultimately death. At present, the cause of AD is only speculated. The real cause is still unclear, and the current treatment can only delay the rate of deterioration, but the disease cannot be cured. Although previous genome-wide association studies have identified several risk factors for this disease, these factors can only be used to predict the risk of an individual. The biggest disadvantage of such approaches is that only the effects of a single factor are considered and the effects of gene combinations are ignored. The current research method for the discovery of compound effect, that is, Epistasis, whether based on statistical linear methods or machine learning methods, requires a large memory capacity to detect all possible combinations, and the combination is usually limited to two elements. In this regard, this study aims at replacing current methods for epistasis with multi-layer perceptron, which is one kind of deep learning methods, to predict individual phenotypes from its genotype data, as well as interpreting certain epistasis from our model. The material for this study was derived from 364 individuals in Alzheimer’s Disease Neuroimaging Initiative (ADNI). These individuals were diagnosed as AD or cognitively normal (CN) control. This study established single-gene model and cross-gene model in sequence, and found within-gene and cross-gene epistasis from these two models. The genetic features found included some known important risk factors, such as APOE-ε4. This proves that the deep learning model of this study can indeed find some important combinations in real data. In addition, this research method has also found combinations with more than two elements, which solves the limitation of the current methods.
| Identifer | oai:union.ndltd.org:TW/106NTU05114014 |
| Date | January 2018 |
| Creators | Yi-Chun Chen, 陳奕均 |
| Contributors | Yen-Jen Oyang, Chien-Yu Chen, 歐陽彥正, 陳倩瑜 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 49 |
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