English(EN) Tree Depth and Sample-Quality-Associated Features: Two Hidden Factors in Boruta Selection for RNA-Seq Data Classification

Boruta基因选择方法的性能受树深度的影响

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

一篇新研究论文探讨了Boruta基因选择方法中树深度设置对RNA-Seq数据分类的影响。研究发现，限制树深度（一种常见做法）会显著改变基因选择，并降低所得基因面板的准确性。研究人员建议使用无上限的树或基于实际深度的上限，并结合类内质量过滤器，以提高Boruta的性能。 AI

影响研究结果表明，RNA-Seq数据分析中的基因选择准确性有潜在的改进空间，将影响生物信息学和计算生物学研究。

排序理由该集群包含一篇研究论文，详细介绍了特定机器学习方法（Boruta）及其参数的研究结果。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Tareque Mohmud Chowdhury, Abu Raihan Mostofa Kamal · 2026-10-28 04:00

树深度和样本质量相关特征：Boruta选择用于RNA-Seq数据分类的两个隐藏因素

arXiv:2610.33307v2 Announce Type: new Abstract: Boruta is a widely used method for selecting gene panels from RNA sequencing data, and its most used implementation recommends Random Forest trees of depth 3 to 7, an untested setting. We tested whether this setting changes which ge…