English(EN) Traditional machine learning vs. deep learning from dynamic graph representations of proteins' 3D folds in the task of protein structure classification

传统机器学习和深度学习在蛋白质结构分类中表现持平

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-29 04:00

一项新的arXiv研究使用动态图表示，对比了用于蛋白质结构分类的传统机器学习（ML）和深度学习（DL）。研究发现，对于大多数数据集，传统ML和DL在准确性方面表现相似，而DL速度显著较慢。这项工作首次在动态蛋白质结构网络的背景下，针对此特定任务直接评估了这两种方法。 AI

排序理由该聚类包含一篇详细介绍机器学习技术比较研究的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Aydin Wells, Francis A. Gatsi, Aaron Striegel, Tijana Milenkovi\'c · 2026-05-29 04:00

Traditional machine learning vs. deep learning from dynamic graph representations of proteins' 3D folds in the task of protein structure classification

arXiv:2605.29228v1 Announce Type: new Abstract: Protein structure classification (PSC) uses supervised learning to predict a protein's CATH/SCOP(e) class from the protein's sequence or 3D structural feature(s). We already modeled 3D structures as (static) protein structure networ…

报道来源 [1]

Traditional machine learning vs. deep learning from dynamic graph representations of proteins' 3D folds in the task of protein structure classification

相关实体

相关话题