用优化算法高效发现基因间高阶互作关系。
High-Order Epistasis Detection Using Factorization Machine with Quadratic Optimization Annealing and MDR-Based Evaluation
- 将基因互作检测转为黑箱优化问题，用因子分解机求解。
- 在有限迭代内成功识别出预设的高阶基因互作模式。
- 适合大规模基因数据中复杂互作关系的快速筛查。
由于候选位点组合的组合爆炸，检测高阶基因互作是遗传关联研究中的基本挑战。尽管多因素降维（MDR）是广泛使用的互作评估方法，但随着位点数或互作阶数增加，穷举式MDR搜索变得计算不可行。本文将互作检测问题定义为黑箱优化问题，并采用带二次优化退火的因子分解机（FMQA）求解。提出一种基于FMQA的高效互作检测方法，以MDR计算的分类误差率（CER）作为黑箱目标函数。在具有预设高阶互作的模拟病例对照数据集上进行实验评估。结果表明，该方法在有限迭代次数内成功识别出不同互作阶数和位点数量下的真实互作模式。这说明所提方法在高阶互作检测中有效且计算高效。
原文摘要 · Abstract (English)
Detecting high-order epistasis is a fundamental challenge in genetic association studies due to the combinatorial explosion of candidate locus combinations. Although multifactor dimensionality reduction (MDR) is a widely used method for evaluating epistasis, exhaustive MDR-based searches become computationally infeasible as the number of loci or the interaction order increases. In this paper, we define the epistasis detection problem as a black-box optimization problem and solve it with a factorization machine with quadratic-optimization annealing (FMQA). We propose an efficient epistasis detection method based on FMQA, in which the classification error rate (CER) computed by MDR is used as a black-box objective function. Experimental evaluations were conducted using simulated case-control datasets with predefined high-order epistasis. The results demonstrate that the proposed method successfully identified ground-truth epistasis across various interaction orders and the numbers of genetic loci within a limited number of iterations. These results indicate that the proposed method is effective and computationally efficient for high-order epistasis detection.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。