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Combination Therapies and Personalized Medicine02:50

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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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Updated: Jun 15, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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基因选择算法用于微阵列癌症分类,使用改进的粒子群优化算法.

Arfan Ali Nagra1, Ali Haider Khan1, Muhammad Abubakar2

  • 1Faculty of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan.

Scientific reports
|August 23, 2024
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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自我惯性重量自适应粒子群优化 (SIW-APSO) 算法,用于癌症数据中的精确基因选择. SIW-APSO-ELM方法提高了利用微阵列数据进行癌症分类的预测准确度.

关键词:
在ELM中,可以选择ELM.进化算法是一种进化算法.改进的公共服务业务微阵列癌症的微阵列癌症在SVM中,SVM是SVM.

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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
  • 机器学习 机器学习

背景情况:

  • 基因选择对于从微阵列数据准确地分类癌症至关重要.
  • 传统的粒子群优化 (PSO) 方法在基因选择中面临冗余性和复杂性的挑战.
  • 开发高效的算法对于分析复杂的基因表达模式至关重要.

研究的目的:

  • 为增强基因选择提出了一种新的自我惯性重量自适应粒子群优化 (SIW-APSO) 算法.
  • 使用微阵列数据提高癌症分类的预测准确度.
  • 为了在粒子群中平衡探索和利用,优化基因选择.

主要方法:

  • 为了优化基因选择,开发了一种新的PSO变体SIW-APSO.
  • 极端学习机器 (ELM) 被集成为基因选择程序.
  • 拟议的SIW-APSO-ELM算法应用于癌症微阵列数据集.

主要成果:

  • SIW-APSO-ELM算法在癌症基因选择中实现了高预测准确度.
  • 与现有最先进的技术相比,提出的方法显示出更高的性能.
  • 通过减少冗余性实现了预后基因的有效识别.

结论:

  • SIW-APSO-ELM算法为癌症分类中的基因选择提供了一种有效的方法.
  • 这种方法提高了分析基因表达数据的准确性和效率.
  • 该研究强调了适应性PSO变体在生物信息学应用中的潜力.