对比深度学习与传统机器学习在动态蛋白质结构网络上的分类表现
Traditional machine learning vs. deep learning from dynamic graph representations of proteins' 3D folds in the task of protein structure classification
- 用动态蛋白质结构网络提取特征，比较深度学习与传统机器学习
- 多数数据集上两者准确率接近，但深度学习速度慢10倍以上
- 首次在动态结构网络上系统评估两类方法，适合结构生物学家参考
蛋白质结构分类（PSC）通过监督学习从蛋白质序列或三维结构特征预测其CATH/SCOPe类别。我们已将三维结构建模为静态蛋白质结构网络（PSNs），证明基于PSN的特征在PSC任务中可媲美序列或直接三维结构特征。最近，我们展示了从动态PSNs中提取的特征优于静态PSNs（从而也优于序列和直接三维特征）。该动态方法使用传统机器学习（ML），结合人工设计特征与现成分类器。本文评估了从动态PSNs出发的自动深度学习（DL）是否带来提升。我们在72个数据集（共约44,000个带有CATH或SCOPe标签的动态PSNs）上进行评估，结果显示，在分类准确率上，传统ML与DL在绝大多数数据集上几乎持平，而深度学习平均慢10倍以上。我们是首个在动态PSN基础上评估传统机器学习与深度学习的团队。
原文摘要 · Abstract (English)
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 networks (PSNs), demonstrating the competitiveness of PSN-based features to sequence or direct (i.e. non-network) 3D structural features in the PSC task. More recently, we demonstrated the power of features extracted from dynamic PSNs over features extracted from static PSNs (and thus by transitivity over sequence and direct 3D structural features) in the same task. That dynamic PSN approach used traditional machine learning (ML), combining manual (pre-engineered) features with an off-the-shelf classifier. Here, we evaluate whether automatic deep learning (DL) from the dynamic PSNs yields improvements. Our evaluation on 72 datasets spanning ~44,000 CATH- or SCOPe-labeled dynamic PSNs reveals that in terms of PSC accuracy, traditional ML and DL are (close to) tied for a large majority of the datasets, while DL is on average 10+ times slower. We are the first to evaluate traditional ML vs. DL in the dynamic PSN-based PSC task.
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