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HB-PLS:通过将Huber损失和Berhu惩罚与部分最小平方回归集成来识别生物过程或途径调节者的统计方法

Wenping Deng¹, Kui Zhang², Cheng He³

¹College of Forest Resources and Environmental Science, Michigan Technological University, Houghton, Michigan 49931, United States of America.

Forestry research

|November 11, 2024

概括

我们开发了HB-PLS回归来从复杂的基因表达数据中识别关键调节基因. 这种新方法有效地识别了植物通路的已知调节者,优于现有技术.

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
系统生物学系统生物学

背景情况:

基因表达数据带来了诸如高维度和多线性等挑战.
准确识别调节基因对于理解生物过程至关重要.

研究的目的:

开发一种用于模拟调节基因和通路基因之间的关系的新方法.
为了解决分析高维基因表达数据的现有方法的局限性.

主要方法:

将Huber损失函数和Berhu惩罚 (HB) 集成到部分最小平方 (PLS) 框架中,创建HB-PLS回归.
开发了一个加速的近接梯度下降算法,以实现高效的优化.
应用HB-PLS来分析Arabidopsis thaliana中的素生物合成和光合作用途径的基因表达数据.

主要成果:

在HB-PLS中,已成功识别出众多已知的阳性通路调节者.
在识别已知的调节者方面,HB-PLS表现出比稀疏部分最小平方 (SPLS) 更高的有效性.
无论是HB-PLS还是SPLS都确定了独特的监管机构,强调了它们的互补作用.

结论:

HB-PLS回归是从高通量基因表达数据中识别途径调节者的有效方法.
统计,机器学习和凸合优化的整合为生物数据分析提供了有前途的方法.
HB-PLS略高于SPLS,这两种方法都是基因发现的宝贵工具.

关键词:

贝尔胡的罚球是惩罚性的.哈伯的损失哈伯的损失胡伯-贝胡部分最小平方.部分最小平方回归.路径监管机构路径监管机构

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