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整合图像处理与深层卷积神经网络用于基因选择和癌症分类,使用微阵列数据.

Yuanyuan Zhang1, Jing Chen1, Chong Zhang2

  • 1School of Medical, Technology and Information Engineering, Zhejiang Chinese Medical University, HangZhou, 310053, China.

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概括
此摘要是机器生成的。

这项研究引入了一种基因优化神经框架 (GONF),用于使用微阵列数据的癌症基因组学. 这种新的方法有效地选择基因并分类癌症亚型,实现高精度和减少错误.

关键词:
癌症的分类 癌症的分类卷积神经网络是一种卷积神经网络.深度神经网络是一种深度神经网络.基因选择 基因选择基因组分析 基因组分析微阵列技术是微阵列技术.mRMRMR 在线观看

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科学领域:

  • 基因组学就是基因组学.
  • 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:

  • 微阵列技术能够进行大规模的基因表达分析,但面临着高维度,噪声和稀疏性等挑战.
  • 强大的分析方法对于从复杂的基因组数据中提取有意义的生物学见解至关重要.
  • 图像处理有助于从视觉数据中识别模式,支持癌症研究中的生物标志物发现.

研究的目的:

  • 开发一种新的基因选择和癌症分类框架,使用深度神经网络和微阵列数据.
  • 解决基因组数据集中高维度,噪声和稀疏性的固有挑战.
  • 为了提高癌症亚型预测的准确性和效率.

主要方法:

  • 基因选择的最小冗余最大相关性 (mRMR) 与深度卷积神经网络 (CNN) 的整合.
  • 应用先进的预处理技术和超参数优化,以提高性能.
  • 关于癌症基因组图谱 (TCGA) 和自闭症脑成像数据交换 (AHBA) 数据集的验证.

主要成果:

  • 基因优化神经框架 (GONF) 实现了高分类准确性:TCGA上97%和AHBA数据集上95%.
  • 与现有方法相比,虚假阳性和虚假阴性率的显著降低.
  • 经过证明的坚固性和适应性,提供生物学上可解释的结果.

结论:

  • 拟议的GONF框架有效地处理用于癌症基因组学的高维,杂和稀疏的微阵列数据.
  • 在癌症亚型预测方面,GONF提供了更高的准确性和可靠性.
  • 该框架显示了在基因组研究和临床环境中更广泛应用的潜力.