预测结直肠癌患者抗癌药物疗效的混合深度模型

IF 0.2 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
A. Karthikeyan, S. Jothilakshmi, S. Suthir
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引用次数: 0

摘要

癌症在基因上是多样化的,因此由于基因的差异,抗癌治疗对人们的疗效也不同。本工作的主要目的是预测结直肠癌患者的抗癌药物疗效,降低死亡率,为患者提供免疫能量。本文提出了一种新的结直肠癌患者抗癌药物疗效体系。使用Min-Max归一化技术对输入数据基因进行归一化,该技术对不同尺度的数据进行归一化。随后,提出了一种改进的基于熵的特征来评估数据的不确定性分布,该特征引入权重来克服计算复杂性的问题。与此特征一起,还检索了基于相关性的特征和统计特征。随后,提出了一种基于混合机器学习的递归特征消除(RFEHML)机制,通过结合决策树和逻辑回归的混合机器学习策略消除递归特征来选择合适的特征集。此外,基尼杂质用于对特征进行排序,并通过消除最小获得的重要分数来选择最大重要分数。在此基础上,提出了利用训练好的特征集预测药物效率的混合模型。该混合模型包括长短期记忆(LSTM)和更新的整流线性单元-深度卷积神经网络(UReLU-DCNN)模型,其中DCNN通过更新全连接层的激活函数来修正。因此,学习特征通过判断患者对药物是否有反应来预测结直肠癌患者的抗癌疗效。最后,将所提出的RFEHML模型的性能与其他传统方法进行了比较。研究发现,所开发的方法在每个学习百分比上都有较高的准确率,60LP = 92.48%, 70LP = 94.28%, 80LP = 95.24%, 90LP = 96.86%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid deep model for predicting anti-cancer drug efficacy in colorectal cancer patients
Cancers are genetically diversified, so anticancer treatments have different levels of efficacy on people due to genetic differences. The main objective of this work is to predict the anticancer drug efficiency for colorectal cancer patients to reduce the mortality rates and provides immune energy for the patients. This paper proposes a novel anti-cancer drug efficacy system in colorectal cancer patients. The input data gene is normalized with the Min–Max normalization technique that normalizes the data in distinct scales. Subsequently, proposes an improved entropy-based feature to evaluate the uncertainty distribution of data, in which it induces weight to overcome the issue of computational complexity. Along with this feature, a correlation-based feature and statistical features are also retrieved. Subsequently, proposes a Recursive Feature Elimination with Hybrid Machine Learning (RFEHML) mechanism for selecting the appropriate feature set by eliminating the recursive features with the aid of hybrid Machine Learning strategies that combine decision tree and logistic regression. Also, the Gini impurity is employed for ranking the feature and selecting the maximum importance score by eliminating the least acquired importance score. Further, proposes a hybrid model for predicting the drug efficiency with the trained feature set. The hybrid model comprises of Long Short-Term Memory (LSTM) and Updated Rectified Linear Unit-Deep Convolutional Neural Network (UReLU-DCNN) model, in which DCNN is modified by updating the activation function at the fully connected layer. Consequently, the learned feature predicts the drug efficacy of anti-cancer in colorectal cancer patients by determining whether the patient is a responder or non-responder of the drug. Finally, the performance of the proposed RFEHML model is compared with other traditional approaches. It is found that the developed method has higher accuracy for each learning percentage, with values of 60LP = 92.48%, 70LP = 94.28%, 80LP = 95.24%, and 90LP = 96.86%, respectively.
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来源期刊
Web Intelligence
Web Intelligence COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
0.90
自引率
0.00%
发文量
35
期刊介绍: Web Intelligence (WI) is an official journal of the Web Intelligence Consortium (WIC), an international organization dedicated to promoting collaborative scientific research and industrial development in the era of Web intelligence. WI seeks to collaborate with major societies and international conferences in the field. WI is a peer-reviewed journal, which publishes four issues a year, in both online and print form. WI aims to achieve a multi-disciplinary balance between research advances in theories and methods usually associated with Collective Intelligence, Data Science, Human-Centric Computing, Knowledge Management, and Network Science. It is committed to publishing research that both deepen the understanding of computational, logical, cognitive, physical, and social foundations of the future Web, and enable the development and application of technologies based on Web intelligence. The journal features high-quality, original research papers (including state-of-the-art reviews), brief papers, and letters in all theoretical and technology areas that make up the field of WI. The papers should clearly focus on some of the following areas of interest: a. Collective Intelligence[...] b. Data Science[...] c. Human-Centric Computing[...] d. Knowledge Management[...] e. Network Science[...]
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