使用结构学习和非凸规化的子空间学习:在基因选择中,混合技术与繁殖优化
Amir Moslemi1, Mahdi Bidar2, Arash Ahmadian3
1Department of Physics, Ryerson University, Toronto, ON, Canada.
Computers in biology and medicine
|August 3, 2023
概括
这项研究引入了一种新的混合基因选择方法,该方法结合了无监督的非凸调整的非负矩阵因子化和结构学习 (NCNMFSL) 与繁殖优化 (MRO). 该方法有效地识别癌症数据集中的歧视性基因,提高了分类准确性.
科学领域:
- 计算生物学 计算生物学
- 机器学习 机器学习
- 生物信息学是一种生物信息学.
背景情况:
- 高维基因选择在癌症研究中至关重要.
- 现有的特征选择方法 (过器,包装器,嵌入式) 有局限性.
- 混合方法可以提高基因选择性能.
研究的目的:
- 提出一种新的混合基因选择方法,结合过器和包装器的策略.
- 改进癌症数据集信息基因的识别.
- 在基准数据集上评估拟议方法的有效性.
主要方法:
- 开发了一种无监督过阶段方法:非凸规则化的非负矩阵因子化和结构学习 (NCNMFSL).
- 在封装阶段使用繁殖优化 (MRO) 来选择特征子集.
- 组合NCNMFSL用于过不相关的特征和MRO用于选择歧视性的特征.
主要成果:
- 混合方法在多个癌症数据集中实现了高精度 (例如,乳腺0.97,结肠和前列腺0.98).
- 在乳腺,心脏,结肠,白血病,前列腺,毒素-171和GLI-85数据集上分别获得了0.97,0.84,0.98,0.95,0.98,0.87和0.85的最高准确度.
- 与最先进的特征选择技术相比,证明了卓越的性能.
结论:
- 拟议的混合基因选择方法对于高维的癌症数据集是有效和熟练的.
- NCNMFSL和MRO的组合为识别关键基因提供了一个强大的方法.
- 这种方法显示了促进癌症研究和个性化医学的巨大潜力.
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