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基因AI 3.0:强大的,新的,通用的混合和集团深度学习框架用于miRNA物种的静止模式从核酸的分类

Jaskaran Singh¹, Narendra N Khanna², Ranjeet K Rout³

¹Department of Computer Science, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India.

Scientific reports

|March 27, 2024

概括

一种新的方法,GeneAI 3.0,使用集体机器学习和深度学习准确地分类微RNA (miRNA) 物种. 这种方法增强了miRNA序列的特征提取,优于以前的方法.

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
基因组学就是基因组学.

背景情况:

分类微RNA (miRNA) 物种,如人类,大猩猩,老鼠和老鼠,由于复杂的序列关系而具有挑战性.
现有的分类方法缺乏稳定性和准确性.

研究的目的:

介绍AtheroPoint的GeneAI 3.0,这是一个用于miRNA序列分类的新和通用方法.
在集成机器学习 (EML) 和深度学习 (DL) 框架内,利用 purin 和 pyrimidine 模式从 miRNA 序列中增强特征提取.

主要方法:

基因AI 3.0利用传统 (,不相似性,能量,同质性,对比度) 和当代 (香农,赫斯特指数,碎形维度) 的特征来创建一个复合特征集.
开发了11个新的分类器 (5个EML,6个EDL) 用于二进制/多类分类,与38个模型 (SML,DL,HDL) 进行基准对比.
采用可解释AI (XAI) 和统计测试来验证四个假设.

主要成果:

24个DL分类器的平均性能 (精度/AUC) 的顺序是EDL > HDL > SDL.
带有CNN层的EDL模型比没有CNN层的模型表现出色0.73%/0.92%.
在EML模型中,比SML显著改善 (ACC/AUC: +6.24%/+6.46%),而EDL模型的表现优于EML模型 (ACC/AUC: +7.09%/+6.96%).

结论:

使用复合特征的集体模型对于miRNA序列分类非常有效和通用.
基因AI 3.0表现出强大的性能,并提供可解释的XAI特征图.
这项研究验证了集体和深度学习方法在miRNA分类中的优越性.

关键词:

复合特征提取复合特征提取整合深度学习的组合.整合机器学习机器学习统计测试统计测试统计测试验证验证验证验证 miRNA分类的分类方法

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