一种通过基因网络数据选负训练样本来提高癌症基因关联的预测性能的方法
Mingzhe Xu1, Nor Aniza Abdullah2, Aznul Qalid Md Sabri2
1Faculty of Computer Science & Information Technology, Universiti Malaya, Kuala Lumpur, 50603 Malaysia; School of Energy and Intelligence Engineering, Henan University of Animal Husbandry and Economy, #6 North Longzihu Rd, Zhengzhou 450000, China.
Computational biology and chemistry
|December 28, 2023
概括
这项研究引入了一种用于癌症基因关联预测中选择负样本的新方法. 通过使用基因网络和图形理论,这种方法提高了机器学习模型的性能,用于识别致癌基因.
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:
- 机器学习模型对于预测癌症基因关联至关重要.
- 提高培训数据质量是提高模型性能的关键.
- 目前的方法主要集中在选择阳性样本 (癌症驱动基因).
研究的目的:
- 开发一种改进的方法来选择癌症基因关联预测中的负样本.
- 提高用于癌症基因识别的机器学习模型的性能.
- 为现有的积极抽样选择方法提供一种补充方法.
主要方法:
- 利用基因网络和图形理论算法来选低癌症相关基因.
- 使用具有较低癌症相关性的遗传数据作为负训练样本.
- 将拟议的负样本选择与现有的癌症基因预测技术相结合.
主要成果:
- 拟议的方法有效地选负样本,提高预测性能.
- 使用这些负样本来训练癌症基因分类模型可以提高准确性.
- 该方法在与最先进的预测技术相结合时,显示出显著的性能改进.
结论:
- 开发的低癌症相关基因查方法为改善负样本选择提供了一种有价值的方法.
- 这种技术可以很容易地与现有的方法集成,用于增强癌症基因预测.
- 这些发现表明,在癌症基因组学中推进机器学习应用的巨大潜力.
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