通过卷积神经网络增强癌症亚型分类:对TCGA基因表达数据进行深入分析
Changda Li1, Yan Yan2, Wenjun Lin3
1Mathematics and Statistics, Thompson Rivers University, 805 TRU Way, Kamloops, BC V2C 0C8 Canada.
Health information science and systems
|May 1, 2025
概括
深度洞察有效地使用基因表达数据对癌症亚型进行分类,优于传统模型. 这种方法将数据转化为图像,以识别用于亚型分类的关键基因.
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:
- 高维基因表达数据对准确的癌症亚型分类提出了挑战.
- 现有的计算模型可能无法完全捕捉基因组数据中的复杂模式.
研究的目的:
- 通过基因表达数据,调整和评估DeepInsight的多类癌症亚型分类.
- 将DeepInsight的性能与已建立的机器学习模型进行比较.
- 识别与不同癌症亚型相关的关键基因.
主要方法:
- 深度洞察被调整为通过将其转换为图像表示来处理基因表达数据.
- 性能与支持向量机,LightGBM,神经网络和决策树进行了评估.
- 开发了一种使用聚合类激活地图的新型多类特征选择方法.
- 对已识别的关键基因进行了基因本体学分析.
主要成果:
- 在乳腺癌,肺癌和结肠癌数据集中,DeepInsight总是比传统模型获得更高的F1分数.
- 该方法成功地解决了多个类别的分类挑战.
- 通过多种分类方法确定了几种重要的基因.
- 基因本体学分析揭示了关键基因的生物学作用.
结论:
- 适应的DeepInsight通过利用基因表达数据的基于图像的表示来提供癌症亚型分类的新方法.
- 聚类激活地图有效地确定了基因发现的关键特征.
- 深度洞察表明,它是癌症亚型分类和生物相关基因识别的有价值工具.
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