一个多分类的深度神经网络用于从高维度,小样本和不平衡的基因微阵列数据中识别癌症类型
Yifu Zeng1,2, Yixiang Zhang3, Zikai Xiao1
1Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China.
Scientific reports
|February 12, 2025
概括
这项研究引入了一种新的多分类生成对抗网络与特征捆绑 (MGAN-FB) 以改进基因微阵列数据的癌症诊断,有效地解决高维度,小样本和不平衡数据的挑战.
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 机器学习
背景情况:
- 基因微阵列数据提出了诸如高维度,小样本大小和多类不平衡等挑战,阻碍了精确的癌症诊断.
- 传统的特征选择和分类算法与这些复杂的数据特征作斗争,导致性能低于最佳.
- 深度学习,特别是生成对抗网络 (GAN),在生物信息学中表现有前途,但在多类应用和高维稀疏数据中是有限的.
研究的目的:
- 提出一种新的多分类生成对抗网络与特征捆绑 (MGAN-FB) 模型相结合.
- 为了应对基因微阵列数据分类中高维度,小样本和多类不平衡的挑战.
- 提高特征提取和分类性能,无论是在特征和算法层面.
主要方法:
- 为发电机开发了一个深度编码器结构,结合了特征捆绑 (FB) 和挤压和激发 (SE) 机制,用于自适应性特征提取.
- 在区分器中实现了一个多分类器与软max模块.
- 设计了一个包含编码,重建,歧视和多类分类损失的新型目标函数,以将GAN框架扩展到多类问题.
主要成果:
- 拟议的MGAN-FB模型实现了有效的尺寸缩小,而没有显著的信息损失.
- 对八种基因微阵列数据集的实验表明,与其他七种方法相比,分类性能优越.
- 该模型在分类准确性,运行时间和统计学意义方面显示出优势.
结论:
- MGAN-FB模型有效地处理了用于癌症诊断的基因微阵列数据的复杂性.
- 整合特征捆绑和SE机制增强了适应性特征提取.
- 这种方法在将生成对抗网络应用于多类生物信息学问题方面取得了重大进展.
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