IEEE J Biomed Health Inform. 2019 Jan;23(1):416-426. doi: 10.1109/JBHI.2018.2790951. Epub 2018 Jan 8.
Gene-gene interactions (GGIs) are important markers for determining susceptibility to a disease. Multifactor dimensionality reduction (MDR) is a popular algorithm for detecting GGIs and primarily adopts the correct classification rate (CCR) to assess the quality of a GGI. However, CCR measurement alone may not successfully detect certain GGIs because of potential model preferences and disease complexities. In this study, multiple-criteria decision analysis (MCDA) based on MDR was named MCDA-MDR and proposed for detecting GGIs. MCDA facilitates MDR to simultaneously adopt multiple measures within the two-way contingency table of MDR to assess GGIs; the CCR and rule utility measure were employed. Cross-validation consistency was adopted to determine the most favorable GGIs among the Pareto sets. Simulation studies were conducted to compare the detection success rates of the MDR-only-based measure and MCDA-MDR, revealing that MCDA-MDR had superior detection success rates. The Wellcome Trust Case Control Consortium dataset was analyzed using MCDA-MDR to detect GGIs associated with coronary artery disease, and MCDA-MDR successfully detected numerous significant GGIs (p < 0.001). MCDA-MDR performance assessment revealed that the applied MCDA successfully enhanced the GGI detection success rate of the MDR-based method compared with MDR alone.
基因-基因相互作用（GGIs）是确定疾病易感性的重要标志物。多因子降维（MDR）是一种用于检测 GGIs 的流行算法，主要采用正确分类率（CCR）来评估 GGI 的质量。然而，由于潜在的模型偏好和疾病复杂性，仅使用 CCR 测量可能无法成功检测到某些 GGIs。在这项研究中，基于多准则决策分析（MCDA）的 MDR 被命名为 MCDA-MDR，并被提出用于检测 GGIs。MCDA 促进 MDR 同时采用 MDR 双向列联表中的多个措施来评估 GGIs；采用 CCR 和规则效用度量。交叉验证一致性用于确定 Pareto 集中最有利的 GGIs。进行了模拟研究以比较基于 MDR 仅的度量和 MCDA-MDR 的检测成功率，结果表明 MCDA-MDR 具有更高的检测成功率。使用 MCDA-MDR 分析了惠康信托病例对照协会数据集，以检测与冠状动脉疾病相关的 GGIs，MCDA-MDR 成功检测到许多显著的 GGIs（p < 0.001）。MCDA-MDR 的性能评估表明，与单独使用 MDR 相比，应用的 MCDA 成功提高了基于 MDR 的方法的 GGI 检测成功率。
