修改量子扩展卷积神经网络用于癌症预测,使用基因表达数据
Magendiran N1, Karthik R2, Dhanalakshmi V3
1Department of Computer Science and Technology, Vivekanandha College of Engineering for Women (Autonomous), Tiruchengode, Namakkal, Tamil Nadu, India.
概括
这项研究引入了经过修改的量子扩展卷积神经网络 (QDCNN),用于使用基因表达数据准确检测癌症,达到90.6%的准确率. 该模型有效预测癌症,在计算瘤学方面提供了有前途的进展.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 机器学习在医疗保健中的应用
背景情况:
- 基因表达数据对于理解癌症生物学至关重要.
- 从基因表达数据准确检测癌症仍然是一个挑战.
- 深度学习模型显示了分析复杂生物数据集的潜力.
研究的目的:
- 提出一个修改的量子扩展卷积神经网络 (QDCNN),用于增强癌症检测.
- 利用基因表达数据来提高诊断准确度.
- 评估拟议的QDCNN模型在癌症数据集上的性能.
主要方法:
- 利用了来自PANCAN数据集的基因表达数据.
- 应用适应式Box-Cox转换用于数据预处理.
- 与Kulczynski一起使用深度神经网络 (DNN) 进行特征融合.
- 将精细的特征入改进的QDCNN,用于癌症预测.
主要成果:
- 经过修改的QDCNN实现了90.6%的准确性.
- 实现了89.0%的真实阳性率 (TPR).
- 报告了0.109的虚假负率 (FNR) 和89.9%的马修斯相关系数 (MCC).
结论:
- 修改后的QDCNN在从基因表达数据中检测癌症方面表现出高效率.
- 拟议的方法在癌症预测准确度上有了显著的改进.
- 这种方法有可能在癌症诊断中得到临床应用.
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