标题： 生物序列中多元分布建模的统一框架 显示英文标题

标题： Unified framework for modeling multivariate distributions in biological sequences

摘要： 揭示生物序列的功能位点，如进化保守、结构相互作用或共同进化的蛋白质位点，是一个基础且具有挑战性的任务。 不同的框架和模型被开发出来以应对这一挑战，包括位置特异性评分矩阵、马尔可夫随机场、多元高斯模型以及最近的自编码器。 这些方法各有优势，尽管它们为更好的生物预测提供了一套见解，但这些见解仅限于相应的方法，并难以转化为互补领域。 在这里，我们提出了上述模型的统一框架，该框架允许在不同方法之间进行可解释的转换，并自然地结合了不同社区中单独获得的优势和见解。 我们展示了如何通过使用统一框架，在实现蛋白质结构预测的最先进性能的同时，提高预测过程的可解释性。 显示英文摘要

摘要： Revealing the functional sites of biological sequences, such as evolutionary conserved, structurally interacting or co-evolving protein sites, is a fundamental, and yet challenging task. Different frameworks and models were developed to approach this challenge, including Position-Specific Scoring Matrices, Markov Random Fields, Multivariate Gaussian models and most recently Autoencoders. Each of these methods has certain advantages, and while they have generated a set of insights for better biological predictions, these have been restricted to the corresponding methods and were difficult to translate to the complementary domains. Here we propose a unified framework for the above-mentioned models, that allows for interpretable transformations between the different methods and naturally incorporates the advantages and insight gained individually in the different communities. We show how, by using the unified framework, we are able to achieve state-of-the-art performance for protein structure prediction, while enhancing interpretability of the prediction process.