标题： 基于核的条件生物序列模型评估 显示英文标题

标题： Kernel-Based Evaluation of Conditional Biological Sequence Models

摘要： 我们提出了一组基于核的工具，用于评估条件序列模型的设计并调整其超参数，重点在于计算生物学中的问题。 我们的工具的核心是一种新的度量方法，用于衡量真实条件分布与模型估计之间的差异，称为增强条件最大均值差异（ACMMD）。 只要可以对模型进行采样，就可以从数据中无偏地估计ACMMD，以量化模型的绝对拟合程度，整合到假设检验中，并用于评估模型的可靠性。 我们通过分析一个流行的蛋白质设计模型ProteinMPNN来展示我们方法的实用性。 我们能够拒绝ProteinMPNN适用于各种蛋白质家族数据的假设，并调整模型的温度超参数以获得更好的拟合效果。 显示英文摘要

摘要： We propose a set of kernel-based tools to evaluate the designs and tune the hyperparameters of conditional sequence models, with a focus on problems in computational biology. The backbone of our tools is a new measure of discrepancy between the true conditional distribution and the model's estimate, called the Augmented Conditional Maximum Mean Discrepancy (ACMMD). Provided that the model can be sampled from, the ACMMD can be estimated unbiasedly from data to quantify absolute model fit, integrated within hypothesis tests, and used to evaluate model reliability. We demonstrate the utility of our approach by analyzing a popular protein design model, ProteinMPNN. We are able to reject the hypothesis that ProteinMPNN fits its data for various protein families, and tune the model's temperature hyperparameter to achieve a better fit.