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微阵列CancerNet:混合优化深度学习与图形CNN与1D-CNN的集成,用于使用微阵列和序列表达数据的癌症分类框架.

B Shyamala Gowri1, S Anu H Nair2, K P Sanal Kumar3

  • 1Department of Computer Science and Engineering, Annamalai University, Annamalainagar, Chidambaram- 608002, Tamil Nadu, India; Assistant Professor, Department of Computer Science and Engineering, Easwari Engineering college, Ramapuram, Chennai-600089, Tamil Nadu, India.

Computational biology and chemistry
|October 19, 2025
PubMed
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DNA Microarrays02:34

DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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这项研究引入了一种使用基因表达数据的新型癌症分类框架. 开发的混合深度学习框架 (HDLF) 达到91.78%的精度,超过现有方法.

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
  • 基因组学就是基因组学.

背景情况:

  • 准确的基因分类对于癌症诊断至关重要,但在高维微阵列数据方面具有挑战性.
  • 现有的基因选择方法在大型数据集中表现出有限的成功.

研究的目的:

  • 用基因表达数据设计一种新的癌症分类框架.
  • 通过先进的计算方法提高癌症分类的准确性和效率.

主要方法:

  • 数据预处理涉及NAN和从微阵列和seq表达式数据中删除缺失值.
  • 修改的砂管优化算法 (MSOA) 用于最佳的基因选择.
  • 一个混合深度学习框架 (HDLF),结合图形卷积神经网络 (GCNN) 和1D卷积神经网络 (1D-CNN),被开发用于分类.

主要成果:

  • 拟议的HDLF框架在癌症分类方面实现了91.78%的精度.
  • MSOA有效地确定了GCNN和1D-CNN的最佳基因和调整参数.
  • 与现有的机器学习和深度学习方法相比,开发的模型表现出卓越的性能.

结论:

  • 综合MSOA和HDLF的新型癌症分类框架显示出显著的前景.
关键词:
癌症分类 癌症分类混合深度学习框架微阵列数据 微阵列数据修改的砂优化算法最佳的基因选择选择

相关实验视频

  • 这种方法为使用基因表达数据准确分类癌症提供了有效的解决方案.
  • 该研究强调了混合深度学习模型在生物信息学中的潜力.