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.

中文翻译：

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一种改进的基于模糊集的多维度降维算法，用于检测上位性。

目的

上位性鉴定对于确定人类遗传疾病的易感性至关重要。技术的飞速发展使得可扩展性使多维度降维（MDR）测量成为实现出色检测的有效计算工具。但是，在多药耐药性操作中对高风险（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，提出了一种改进的使用SDR中隶属度的模糊S型（FS）方法来解决二进制分类的局限性。FS方法与MDR测量相结合产生了更高的分辨潜在多因素基因型相似频率的能力。

结果

我们将我们的结果与其他基于MDR的方法进行了比较，并且FSMDR在模拟数据集上实现了卓越的检测率。结果表明，模糊分类可以提供对MDR操作中H / L分类的不确定性的认识。

结论

FSMDR成功地在Wellcome Trust病例对照协会数据集中检测出明显的冠状动脉疾病上皮。