基于改进模糊集的多因子降维方法用于检测上位性。
An improved fuzzy set-based multifactor dimensionality reduction for detecting epistasis.
机构信息
Department of Electronic Engineering, National Kaohsiung University of Science and Technology, No. 415, Jiangong Rd., Sanmin Dist., Kaohsiung City, 80778, Taiwan; Ph. D. Program in Biomedical Engineering, Kaohsiung Medical University, No. 100, Shih-Chuan 1st Rd., Kaohsiung, 80708, Taiwan.
Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, No.1, Sec. 1, Syuecheng Rd., Dashu District, Kaohsiung, 84001, Taiwan.
出版信息
Artif Intell Med. 2020 Jan;102:101768. doi: 10.1016/j.artmed.2019.101768. Epub 2019 Nov 22.
OBJECTIVE
Epistasis identification is critical for determining susceptibility to human genetic diseases. The rapid development of technology has enabled scalability to make multifactor dimensionality reduction (MDR) measurements an effective calculation tool that achieves superior detection. However, the classification of high-risk (H) or low-risk (L) groups in multidrug resistance operations calls for extensive research.
METHODS AND MATERIAL
In this study, an improved fuzzy sigmoid (FS) method using the membership degree in MDR (FSMDR) was proposed for solving the limitations of binary classification. The FS method combined with MDR measurements yielded an improved ability to distinguish similar frequencies of potential multifactor genotypes.
RESULTS
We compared our results with other MDR-based methods and FSMDR achieved superior detection rates on simulated data sets. The results indicated that the fuzzy classifications can provide insight into the uncertainty of H/L classification in MDR operation.
CONCLUSION
FSMDR successfully detected significant epistasis of coronary artery disease in the Wellcome Trust Case Control Consortium data set.
目的
基因座间相互作用的识别对于确定人类遗传疾病的易感性至关重要。随着技术的快速发展，多维降维（MDR）测量已经成为一种有效的计算工具，实现了优越的检测效果。然而，在多药耐药操作中，高风险（H）或低风险（L）组的分类需要广泛的研究。
方法和材料
在这项研究中，我们提出了一种使用 MDR 隶属度的改进模糊 Sigmoid（FS）方法（FSMDR），以解决二分类的局限性。FS 方法与 MDR 测量相结合，提高了区分潜在多因素基因型相似频率的能力。
结果
我们将结果与其他基于 MDR 的方法进行了比较，FSMDR 在模拟数据集上实现了更高的检测率。结果表明，模糊分类可以深入了解 MDR 操作中 H/L 分类的不确定性。
结论
FSMDR 成功地检测到了 Wellcome Trust 病例对照联合数据集中冠心病的显著基因座间相互作用。
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