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相关概念视频

Genetic Screens02:46

Genetic Screens

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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二进制沙猫群优化算法用于对生物数据进行包装特征选择.

Amir Seyyedabbasi1

  • 1Software Engineering Department, Faculty of Engineering and Natural Science, Istinye University, 34396 Istanbul, Turkey.

Biomimetics (Basel, Switzerland)
|July 28, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种二进制的沙猫群优化 (bSCSO) 算法,用于有效地选择生物数据集中的特征. bSCSO通过识别最佳特征子集来提高分类准确性,优于现有方法.

关键词:
二进制沙猫群优化优化二进制沙猫群优化生物数据 生物数据这是分类分类的分类.功能选择 功能选择这种算法是Metaheuristic算法.优化问题优化问题

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科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 机器学习 机器学习

背景情况:

  • 大量的生物数据集往往包含不相关的,冗余的,或噪声的属性,降低了分类模型的性能.
  • 传统的特征选择方法可能在计算上昂贵,并且由于问题的NP-hard性质,容易产生局部最佳值.

研究的目的:

  • 提出一种高效和有效的全局搜索方法,用于在离散问题中选择特征,特别是对于生物数据.
  • 介绍二进制沙猫群优化 (bSCSO) 算法作为包装器特征选择的解决方案.

主要方法:

  • 开发了一种针对离散优化问题的二进制版本的沙猫群优化 (bSCSO) 算法.
  • 通过使用包装特征选择方法对十个基准生物数据集进行了bSCSO评估.
  • 将bSCSO的性能与四个当代二进制优化算法的性能进行了比较.

主要成果:

  • 对于生物数据集的特征选择,bSCSO算法表现出卓越的性能.
  • 与现有的二进制优化算法相比,实现了更高的预测准确性.
  • 成功识别了更小,更相关的特征子集,减少了计算复杂性.

结论:

  • 拟议的bSCSO算法是生物数据分析中特征选择的有效和高效方法.
  • bSCSO提供了一种有希望的方法来克服传统方法的局限性,提高分类性能.
  • 这项工作为生物信息学中的离散问题提供了一种新的优化技术.