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在韩国基因组和流行病学研究 (KoGES) 中对喘疾病预测的惩罚和机器学习方法的评估
Yongjun Choi1, Junho Cha1, Sungkyoung Choi2,3
1Department of Applied Artificial Intelligence, College of Computing, Hanyang University, 55 Hanyang-daehak-ro, Sangnok-gu, Ansan, 15588, South Korea.
处罚方法显示出比机器学习方法更好的喘预测. 然而,过量采样改善了随机森林和提高了性能,超过了对喘风险的预测准确度的惩罚方法.
科学领域:
- 遗传学 是一个遗传学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 全基因组关联研究 (GWAS) 确定与疾病相关的遗传变异.
- 统计和机器学习方法越来越多地用于疾病预测.
- 使用遗传数据预测喘是研究的一个关键领域.
研究的目的:
- 评估喘预测的惩罚和机器学习方法.
- 使用来自韩国基因组和流行病学研究 (KoGES) 的韩国芯片 (KORV1.1) 数据集.
- 比较各种统计模型的预测性能.
主要方法:
- 使用后勤回归和流行病学因素选择的单核酸多态 (SNP).
- 评估的方法包括,LASSO,弹性网,SCAD,SVM,随机森林,提升,袋装,天真贝斯和k-NN.
- 性能指标:AUC-ROC,精度,回忆,F1得分,科恩卡帕,平衡精度,错误率,MCC和AUC-PR.
- 三个过量采样算法解决了数据不平衡问题.
主要成果:
- 处罚方法在预测喘方面通常优于标准机器学习方法.
- 随机森林和增强方法在应用过量采样技术后显示出优异的预测性能.
- 对比分析涉及多个性能指标来评估模型的准确性.
结论:
- 受到惩罚的方法为从遗传数据中预测喘提供了强有力的基线.
- 过量采样策略显著提高了组合方法的预测能力,例如随机森林和提升.
- 选择方法和数据平衡技术对于准确预测喘风险至关重要.
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