Accurate RNA secondary structure prediction is vital for understanding
cellular regulation and disease mechanisms. Deep learning (DL) methods have
surpassed traditional algorithms by predicting complex features like
pseudoknots and multi-interacting base pairs. However, traditional distance
measures can hardly deal with such tertiary interactions and the currently used
evaluation measures (F1 score, MCC) have limitations. We propose the
Weisfeiler-Lehman graph kernel (WL) as an alternative metric. Embracing
graph-based metrics like WL enables fair and accurate evaluation of RNA
structure prediction algorithms. Further, WL provides informative guidance, as
demonstrated in an RNA design experiment.

准确的 RNA 二级结构预测对于理解细胞调控和疾病机制至关重要。通过预测伪结和多相互作用碱基对等复杂特征，深度学习方法已经超过了传统算法。然而，传统距离度量难以处理如此三级相互作用，目前使用的评估指标（F1 分数，MCC）存在局限性。我们提出 Weisfeiler-Lehman 图核作为一种替代度量标准。采用基于图的度量标准如 WL 可以公正准确地评估 RNA 结构预测算法。此外，WL 在 RNA 设计实验中展示了信息丰富的指导作用。

重新思考 RNA 次级结构问题的性能衡量

Rethinking Performance Measures of RNA Secondary Structure Problems

Current neural architecture search (NAS) strategies focus only on finding a
single, good, architecture. They offer little insight into why a specific
network is performing well, or how we should modify the architecture if we want
further improvements. We propose a Bayesian optimisation (BO) approach for NAS
that combines the Weisfeiler-Lehman graph kernel with a Gaussian process
surrogate. Our method optimises the architecture in a highly data-efficient
manner: it is capable of capturing the topological structures of the
architectures and is scalable to large graphs, thus making the high-dimensional
and graph-like search spaces amenable to BO. More importantly, our method
affords interpretability by discovering useful network features and their
corresponding impact on the network performance. Indeed, we demonstrate
empirically that our surrogate model is capable of identifying useful motifs
which can guide the generation of new architectures. We finally show that our
method outperforms existing NAS approaches to achieve the state of the art on
both closed- and open-domain search spaces.

本文提出一种基于贝叶斯优化的神经架构搜索方法，结合 Weisfeiler-Lehman 图内核和高斯过程代理，在高效，可扩展的数据条件下寻找拓扑结构和对神经网络性能的有益特征进行优化和发现。并且在闭合和开放领域的搜索空间中，表现优于现有的 NAS 方法。