用进化算法优化分子分类，提升预测准确率与泛化能力。
Evolutionary Optimization for the Classification of Small Molecules Regulating the Circadian Rhythm Period: A Reliable Assessment
- 通过进化算法优化特征选择与分类器性能
- 准确率提升，过拟合现象明显减少
- 适合药物研发中的小分子筛选任务
昼夜节律在调控生物过程方面起关键作用，其紊乱与多种健康问题相关。识别影响昼夜节律周期的小分子对开发靶向疗法至关重要。本研究探索了进化优化技术在该类分子分类中的应用。采用进化算法优化特征选择与分类性能，使用多种机器学习分类器，并以准确率和泛化能力为评估指标。结果表明，所提出的进化优化方法相比基线模型显著提升了分类准确率并降低了过拟合。此外，将准确率方差作为惩罚因子可增强模型在真实场景中的可靠性。研究证实，进化优化是分类调节昼夜节律小分子的有效策略，不仅提高预测性能，还确保模型更具鲁棒性。
原文摘要 · Abstract (English)
The circadian rhythm plays a crucial role in regulating biological processes, and its disruption is linked to various health issues. Identifying small molecules that influence the circadian period is essential for developing targeted therapies. This study explores the use of evolutionary optimization techniques to enhance the classification of these molecules. We applied an evolutionary algorithm to optimize feature selection and classification performance. Several machine learning classifiers were employed, and performance was evaluated using accuracy and generalization ability. The findings demonstrate that the proposed evolutionary optimization method improves classification accuracy and reduces overfitting compared to baseline models. Additionally, the use of variance in accuracy as a penalty factor may enhance the model's reliability for real-world applications. Our study confirms that evolutionary optimization is an effective strategy for classifying small molecules regulating the circadian rhythm. The proposed approach not only improves predictive performance but also ensures a more robust model.
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