申请说明:基于TD的UFE和基于TD的UFEadv:用于执行基于张量分解的无监督特征提取的生物导体包
1Department of Physics, Chuo University, Tokyo, Japan.
Frontiers in artificial intelligence
|September 18, 2023
概括
新的R包,基于TD的UFE和基于TD的UFEadv,使得基于张量分解 (TD) 的无监督特征提取 (FE) 可供非专家使用. 这些工具有助于识别差异表达的基因和执行多组学分析,优于现有方法.
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:
- 基于张量分解 (TD) 的无监督特征提取 (FE) 是有效的生物信息任务,如生物标志物识别和基因分析.
- 基于TD的FE的广泛采用一直受到缺乏对没有专业专业知识的研究人员提供用户友好的工具的限制.
研究的目的:
- 在生物信息学中开发基于TD的无监督FE的可访问工具.
- 为了使不熟悉TD的研究人员能够进行先进的分析,例如差异基因表达和多组学分析.
主要方法:
- 开发两个R/生物导体包:基于TD的UFE和基于TD的UFEadv.
- 在用户友好的接口中实现基于TD的无监督FE算法.
- 促进差异基因表达分析和多组学数据集成.
主要成果:
- 开发的包,TDbasedUFE和TDbasedUFEadv,成功地为非专家提供了基于TD的无监督FE.
- 在相关分析中,基于TD的UFE与最先进的方法,如DESeq2和DIABLO相比,表现优越.
- 这些软件包促进了关键的生物信息学任务,包括识别差异表达的基因和多组学分析.
结论:
- 基于TD的UFE和基于TD的UFE降低了在生物信息学研究中利用基于TD的FE方法的入门障碍.
- 这些软件包为基因组学和多基因组学研究中的特征提取和分析提供了强大且易于使用的替代方案.
- 这些工具免费使用,促进了该领域的更广泛应用和进步.
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