评估基于浮纹的算法在检测高阶相互作用方面的局限性.
Philip J Freda1, Suyu Ye2, Robert Zhang3
1Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, 90069, CA, USA.
基于缓解的算法 (RBA) 难以检测复杂的,更高阶的表征,特别是在大型数据集中. 绝对值排名对特定的四向相互作用有希望,但需要进一步开发以获得更广泛的应用.
科学领域:
- 遗传学 是一个遗传学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 表观性或基因与基因的相互作用对于复杂的特征至关重要.
- 调查更高层次的表观症是计算上具有挑战性的.
- 基于缓解的算法 (RBA) 用于特征选择,但有局限性.
研究的目的:
- 评估RBA在检测更高阶的表观性相互作用方面的效率.
- 探索绝对值排名,以更好地检测复杂相互作用.
- 在表现分析中定义RBA的局限性.
主要方法:
- 在模拟的遗传数据上评估ReliefF,MultiSURF和MultiSURFstar.
- 建模的基因型-表型关联与2路至5路相互作用.
- 将RBA与随机混合和相互信息控制进行比较.
主要成果:
- RBA 能够有效地检测低级 (2-3 路) 交互.
- 高阶相互作用检测受到特征数量和噪声的限制.
- 绝对值排名检测到小 (20 个特征) 数据集中的 4 路 XOR 相互作用.
结论:
- 目前的RBA对更高层次的表征有固有的局限性.
- 对大而复杂的数据集需要改进基于Relief的方法.
- 需要进一步的研究来增强表皮病检测能力.
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