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使用深度学习预测人类乳腺癌生存率的整体框架

Ehzaz Mustafa¹, Ehtisham Khan Jadoon¹, Sardar Khaliq-Uz-Zaman¹

¹Department of Computer Science, Comsats University Islamabad, Abbottabad Campus, Islamabad 22060, Pakistan.

Diagnostics (Basel, Switzerland)

|May 27, 2023

概括

本研究引入了使用多模式数据进行乳腺癌存活率预测 (EBCSP) 的整体模型. EBCSP模型有效地预测了患者的结果,优于单一模式的方法.

科学领域:

在瘤学瘤学.
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

乳腺癌是导致死亡的主要原因,需要准确的生存预测,以便有效的治疗计划.
及时预后有助于医生在为乳腺癌患者做出关键治疗决策.
对于乳腺癌预后的高效和快速计算模型有很大的需求.

研究的目的:

为乳腺癌存活率预测 (EBCSP) 提出一个整体模型,利用多模式数据.
开发一个高效和快速的计算工具,用于乳腺癌的预后.
提高乳腺癌存活率预测的准确性.

主要方法:

通过堆叠多个神经网络的输出来开发一个整体模型 (EBCSP).
卷积神经网络 (CNN) 用于临床数据,深度神经网络 (DNN) 用于副本数变异 (CNV),以及长短期记忆 (LSTM) 架构用于基因表达数据.
采用随机森林方法对生存能力进行二元分类 (长期>5年与短期<5年).

主要成果:

EBCSP模型整合了多模式数据,包括临床信息,CNV和基因表达,用于增强预测.
整体方法有效地处理复杂的,多维的生物数据.
与使用单一数据模式和现有基准的模型相比,EBCSP模型表现优越.

结论:

拟议的EBCSP模型为乳腺癌生存率预测提供了一种有效的计算方法.
在整体框架内使用多模式数据显著提高了预测准确性.
EBCSP模型为帮助乳腺癌治疗的临床决策提供了有价值的工具.

关键词:

在美国,CNN是CNN.DNN DNN 在线这是LSTM的LSTM.一个RNN RNN 乳腺癌乳腺癌乳腺癌深度学习是一种深度学习.诊断诊断诊断诊断诊断预后预后预后

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