应用于生物标志物选择的多目标遗传算法的系统过估计调整的双阶段优化器
Dual-stage optimizer for systematic overestimation adjustment applied to multi-objective genetic algorithms for biomarker selection
AI总结：
本文提出DOSA-MO算法，通过在学习过程中调整性能期望来减少多目标遗传算法在生物标志物选择中的系统过估计，并在肾癌和乳腺癌转录组数据上验证其改进模型选择的效果。
摘要：
利用机器学习从组学数据中发现生物标志物的挑战在于分子特征丰富但样本稀缺。机器学习中的大多数特征选择方法需要评估各种特征集（模型）以确定最有效的组合。该过程通常使用验证数据集进行，涉及测试不同的特征集以优化模型性能。评估存在性能估计误差，当选择涉及许多模型时，最佳模型几乎肯定会被过估计。使用特征选择方法进行生物标志物识别可以作为一个多目标问题来处理，需要在预测能力和特征数量的简约性之间进行权衡。遗传算法是多目标优化的常用工具，但它们会演化出大量解，因此容易产生过估计。已有方法被提出用于在单目标问题中模型已被选定后减少过估计，但不存在能够在优化过程中减少过估计、改进模型选择或应用于更一般的多目标领域的算法。我们提出了DOSA-MO，一种新颖的多目标优化包装器算法，该算法学习原始估计、其方差以及解的特征集大小如何预测过估计。DOSA-MO在优化过程中调整性能的期望，改进解集的组成。我们验证了DOSA-MO在预测癌症亚型和/或患者总生存期时，使用三个肾癌和乳腺癌的转录组学数据集，在留出样本集或外部样本集上提高了最先进遗传算法的性能。
英文摘要：
The challenge in biomarker discovery using machine learning from omics data lies in the abundance of molecular features but scarcity of samples. Most feature selection methods in machine learning require evaluating various sets of features (models) to determine the most effective combination. This process, typically conducted using a validation dataset, involves testing different feature sets to optimize the model's performance. Evaluations have performance estimation error and when the selection involves many models the best ones are almost certainly overestimated. Biomarker identification with feature selection methods can be addressed as a multi-objective problem with trade-offs between predictive ability and parsimony in the number of features. Genetic algorithms are a popular tool for multi-objective optimization but they evolve numerous solutions thus are prone to overestimation. Methods have been proposed to reduce the overestimation after a model has already been selected in single-objective problems, but no algorithm existed capable of reducing the overestimation during the optimization, improving model selection, or applied in the more general multi-objective domain. We propose DOSA-MO, a novel multi-objective optimization wrapper algorithm that learns how the original estimation, its variance, and the feature set size of the solutions predict the overestimation. DOSA-MO adjusts the expectation of the performance during the optimization, improving the composition of the solution set. We verify that DOSA-MO improves the performance of a state-of-the-art genetic algorithm on left-out or external sample sets, when predicting cancer subtypes and/or patient overall survival, using three transcriptomics datasets for kidney and breast cancer.
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