开发符号表达式组合用于乳腺癌类型分类使用遗传编程符号分类器和决策树分类器
Nikola Anđelić1, Sandi Baressi Šegota1
1Department of Automation and Electronics, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia.
Cancers
|July 14, 2023
概括
这项研究引入了一种基因编程符号分类器 (GPSC),使用基因表达数据准确分类乳腺癌亚型. 该方法实现了高精度,有助于为乳腺癌患者提供个性化治疗策略.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 机器学习在瘤学中
背景情况:
- 准确的乳腺癌亚型确定对于有效的治疗选择至关重要.
- 使用基因表达来分类乳腺癌是具有挑战性的,因为高维度和数据不平衡.
- 现有的方法在大量基因表达数据集和有限的样本方面存在困难.
研究的目的:
- 利用基因表达数据开发一种高度准确的乳腺癌亚型分类方法.
- 应用遗传编程符号分类器 (GPSC) 来提取可解释的符号表达式 (SE).
- 解决乳腺癌数据集中高维度,小样本大小和类不平衡的挑战.
主要方法:
- 主要组件分析 (PCA) 用于缩小维度.
- 过量采样技术来处理类不平衡和小样本大小.
- 基因编程符号分类器 (GPSC) 具有随机超参数值搜索 (RHVS) 和5倍交叉验证 (5CV).
主要成果:
- 在过量采样数据集上使用GPSC实现了0.992的峰值分类准确性.
- 为乳腺癌亚型分类生成符号表达式 (SE).
- 将SE与决策树分类器相结合,进一步提高了准确度,达到0.994.
结论:
- GPSC是从基因表达数据准确分类乳腺癌亚型的有效工具.
- 开发的方法成功克服了基因组数据分析中的常见挑战.
- 这种方法有望提高诊断准确度,并指导个性化癌症治疗.
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