卷积时间基于深度神经网络 (CTDN):一种即兴堆叠的混合计算方法,用于预测抗癌药物反应
Davinder Paul Singh1, Baijnath Kaushik1
1School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra 182320, Jammu and Kashmir, India.
Computational biology and chemistry
|May 31, 2023
概括
这项研究引入了新的优化算法,用于药物遗传学中的特征选择,改善了癌症药物反应预测. 提出的方法提高了准确性,并减少了个性化癌症治疗的模型复杂性.
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
- 药物基因组学 药物基因组学
背景情况:
- 准确预测化疗药物反应对于个性化癌症治疗至关重要.
- 药物代谢酶和遗传变异的特征有助于预测药物的疗效.
- 现有的特征选择和药物遗传学分类方法需要优化.
研究的目的:
- 评估药物遗传学中特征选择的优化算法.
- 为了比较传统和先进的机器学习模型来预测癌症药物反应.
- 为了引入一个新的卷积时间深度神经网络 (CTDN) 模型.
主要方法:
- 利用了来自癌症细胞系百科全书 (CCLE) 和癌症药物敏感性基因组学 (GDSC) 的数据集.
- 用火和灰狼优化技术来提取特征.
- 比较了传统的机器学习 (ML),集体ML,堆叠算法和拟议的CTDN模型.
主要成果:
- 由火和灰狼优化增强的拟议CTDN模型,超过了现有的最先进的方法.
- 优化算法有效地减少了模型中的多对线性和过拟合.
- 该研究表明,预测化疗药物反应的效率有所提高.
结论:
- 新的优化算法显著提高了药物遗传学分类的特征选择.
- CTDN模型为准确有效地预测癌症药物反应提供了一个有希望的方法.
- 这项研究通过改进的计算药物反应预测,有助于推进个性化医疗.
关键词:
CCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLECCLCCLCCCCLCCCCLCCLCCLCCLCCCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCLCCL is is is is the one who is who is who is who is who is who is who is who is who is who is who is who is who is who is who is who is who is who is who is who is who is who is who is who is who is who isCTDN CTDN 在线在GDSC中,GDSC是指GDSC.机器学习和深度学习算法堆叠堆叠 在堆叠堆叠.更多相关视频
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