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Chemotherapy-Induced Nausea and Vomiting: Neurokinin-1 Receptor Antagonists01:28

Chemotherapy-Induced Nausea and Vomiting: Neurokinin-1 Receptor Antagonists

Neurokinin 1 (NK1) receptors are distributed across the GI tract, vagal afferents, and key CNS regions including the central vomiting center and chemoreceptor trigger zone (CTZ) Chemotherapy agents stimulate enterochromaffin cells in the gastrointestinal (GI) tract to release large amounts of substance P (SP). SP is a neuropeptide released by specific sensory nerves in response to many different stressors, including those in the GI mucosa affected by chemotherapy.  SP binds and activates these...

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MHGCN:一个多通道混合图卷积神经网络用于癌症药物反应预测.

Peisheng Yang, Changxiang He, Ping Zhang

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    概括
    此摘要是机器生成的。

    这项研究引入了一种新的深度学习模型,即多通道混合图卷积神经网络 (MHGCN),通过考虑细胞系-药物对 (CDP) 拓来改进个性化癌症药物反应预测. 在现有方法中,MHGCN显著提高了预测准确度.

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    科学领域:

    • 计算生物学是一种计算生物学.
    • 基因组学就是基因组学.
    • 药理学 药理学是指药理学的学科.

    背景情况:

    • 个性化癌症治疗由于癌细胞异质性而具有挑战性.
    • 深度学习模型有助于个性化治疗,但经常忽视细胞系-药物对 (CDP) 数据中的内在关系.
    • 现有的模型缺乏拓信息的整合,以改善癌症药物反应 (CDR) 预测.

    研究的目的:

    • 提出一种新的多通道混合图卷积神经网络 (MHGCN),用于准确预测癌症药物反应 (CDR).
    • 将细胞系-药物对 (CDP) 的拓关系纳入深度学习框架.
    • 通过改善癌症患者药物疗效的预测来增强个性化治疗.

    主要方法:

    • 通过整合基因表达和药物分子指纹来定义CDP,使用无证自编码器进行细化.
    • 构建了一个CDP相似性网络和连接细胞系和药物的异质响应图.
    • 使用MHGCN与图形卷积层和异质图形卷积神经网络进行特征嵌入和响应预测,然后进行加权矩阵融合.

    主要成果:

    • 拟议的MHGCN框架明确纳入了CDP拓,这是CDR预测的新方法.
    • 与最先进的方法相比,MHGCN在预测准确度方面取得了统计学上显著的改进.
    • 该模型有效地整合了多模式数据和图形结构,以实现强大的CDR预测.

    结论:

    • 通过利用拓信息,MHGCN在预测癌症药物反应方面取得了重大进展.
    • 该框架为癌症治疗计划提供了更准确和个性化的方法.
    • 这项研究强调了在生物数据分析的深度学习模型中考虑网络拓学的重要性.