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系统性高估调整的双阶段优化器应用于用于生物标志物选择的多目标遗传算法

Luca Cattelani¹, Vittorio Fortino¹

¹School of Medicine, Institute of Biomedicine, University of Eastern Finland, Yliopistonranta 1, PO Box 1627, 70211 Kuopio, Finland.

Briefings in bioinformatics

|December 31, 2024

概括

我们开发了一种新的算法,DOSA-MO,以从omics数据中改进生物标志物面板的选择. 它在优化过程中减少了高估错误,导致更准确的癌症亚型和生存预测.

科学领域:

生物信息学是一种生物信息学.
计算生物学计算生物学
机器学习在基因组学中的应用

背景情况:

从omics数据中选择生物标志物面板是具有挑战性的,因为高维度和有限的样本.
包装特征选择方法,就像遗传算法一样,与机器学习一起用于生物标志物发现.
现有的方法往往高估了模型性能,特别是在多目标优化中.

研究的目的:

在优化过程中解决多目标生物标志物选择中的性能高估问题.
为了引入一个新的算法,双阶段优化器系统的高估计调整在多目标问题 (DOSA-MO).
改进生物标志物面板的选择,以提高预测准确度.

主要方法:

开发了DOSA-MO,一个多目标优化包装算法.
在优化过程中,DOSA-MO学会预测和调整性能高估.
通过与最先进的遗传算法进行比较,评估了DOSA-MO.

主要成果:

DOSA-MO显著提高了外部数据集上的遗传算法的性能.
该算法提高了癌症亚型分类的准确性.
观察到患者整体存活时间的预测得到改善.

结论:

在多目标生物标志物选择中,DOSA-MO有效地减少了绩效过高估计.
拟议的方法提高了从omics数据中识别的生物标志物面板的可靠性和准确性.
在癌症研究中,DOSA-MO为机器学习应用提供了宝贵的进步.

关键词:

生物标志物发现发现遗传算法遗传算法模型选择,模型选择.这是一个多重目标的多重目标.俄米克斯 (omicsics) 是一个电子产品.过高估值调整的调整

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