qMIDS 与机器学习分类器的早期融合与晚期融合在基因表达数据口腔癌诊断中的比较
Early vs late fusion of qMIDS with machine learning based classifiers for oral cancer diagnosis from gene expression data
研究比较了 qMIDS 与机器学习分类器融合的两种架构在基因表达数据口腔癌诊断中的表现，并在地理与族群来源不同的数据集上测试。晚期融合在多数匹配模型配置中显著优于早期融合，但在最佳配置下两架构在多数队列中统计上无差异。跨数据集分析显示，训练与测试样本来自不同人群时该架构仍保持稳健。
来源：PLOS Computational Biology · journals.plos.org
by Ferid Ben Ali, Iosif Mporas, Vaishnavi Balaji, Dickson Ibaranyanakaye, Barbara Carey, Muy-Teck Teh, Sola Adeleke
Different fusion architectures of the quantitative Malignancy Index Diagnostic System (qMIDS) method with machine learning classifiers for oral cancer diagnosis from gene expression data are investigated. The proposed fusion architectures are tested on geographically and ethnically diverse datasets and the evaluation results showed performance improvements with late fusion, which significantly outperformed early fusion in more matched model configurations than the reverse in every cohort, although at the best-performing configuration the two architectures were statistically indistinguishable in most cohorts. Inter-dataset analysis showed robustness of the proposed architectures when the training/test samples are from different populations.