机器学习算法和特征选择在KEGG数据库上的优缺点的比较 微生物基因路径注释及其对重建网络拓学的影响
Michael Robben1, Mohammad Sadegh Nasr1, Avishek Das1
1Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas, USA.
概括
机器学习算法显著改善了新物种的基因注释,超过了传统方法. 这些先进的技术为微生物组研究提供了更高的准确性和更快的处理速度.
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:
- 传统的基因注释依赖于同源对齐,这与进化遥远的物种作斗争,特别是在复杂的环境中,如人类肠道微生物群.
- 基因注释的质量随着新型微生物群物种的测序增加而下降,需要先进的计算方法.
- 机器学习 (ML) 为克服当前基因注释技术的局限性提供了一个有希望的替代方案.
研究的目的:
- 评估基因注释的各种经典和非经典机器学习算法的性能.
- 将基于ML的方法与传统技术 (如同源对齐和正源基因聚类) 进行比较.
- 用KEGG数据库评估ML在注释人类微生物组相关物种基因中的实用性.
主要方法:
- 研究了常见的集体,集群和深度学习算法.
- 在KEGG数据库中利用人类微生物组相关物种的基因进行注释任务.
- 与CD-Hit和BLAST对齐相比,对预测准确度,精度回忆和途径相互作用发现进行了比较.
主要成果:
- 大多数研究的ML算法显示,与CD-Hit相比,对部分KEGG功能的预测准确度更高.
- 基于动机的ML方法实现了比同源对齐或正源基因聚类更高的速度和更好的精确回忆.
- 与BLAST对齐相比,渐变增强集团和神经网络发现的新KEGG通路相互作用的数量是BLAST对齐的两倍.
结论:
- 基于动机的机器学习算法为新测序物种的基因注释提供了更快,更准确的替代方案.
- 机器学习方法增强了在微生物群落内发现功能性基因信息和通路相互作用的发现.
- 将ML集成到注释软件中将为研究人员提供微生物组研究的先进工具,超越单独的序列对齐的局限性.
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