有序生物序列的密度估计及其应用
Density estimation for ordinal biological sequences and its applications
AI总结：
针对有序生物序列的分布推断问题，提出基于贝叶斯场论的非参数最大熵扩展方法，经胶质瘤非整倍体验证，可用于位点关联分析和概率景观几何研究。
摘要：
生物序列并非随机产生，而是以特定频率出现，这些频率反映了相关系统或现象的特性。因此，了解生物序列在序列空间中的分布，是理解其潜在机制的自然第一步。本文提出了一种新方法，用于推断生物序列样本所服从的概率分布，适用于序列由具有自然排序的元素构成的场景。该方法基于贝叶斯场论（一种基于物理学的机器学习方法），可被视为传统最大熵估计的非参数扩展。作为示例，我们用该方法分析了来自The Cancer Genome Atlas（癌症基因组图谱）项目的胶质瘤非整倍体数据。此外，我们展示了可基于所得概率分布开展的两项后续分析：其一是探究序列位点间的关联，为我们提供了推断主导生物语法的途径；其二是研究概率景观的全局几何结构，使我们能从进化视角看待该问题。研究表明，该方法能让我们通过序列样本了解现实世界中生物系统或现象的运作机制。
英文摘要：
Biological sequences do not come at random. Instead, they appear with particular frequencies that reflect properties of the associated system or phenomenon. Knowing how biological sequences are distributed in sequence space is thus a natural first step toward understanding the underlying mechanisms. Here we propose a new method for inferring the probability distribution from which a sample of biological sequences were drawn for the case where the sequences are composed of elements that admit a natural ordering. Our method is based on Bayesian field theory, a physics-based machine learning approach, and can be regarded as a nonparametric extension of the traditional maximum entropy estimate. As an example, we use it to analyze the aneuploidy data pertaining to gliomas from The Cancer Genome Atlas project. In addition, we demonstrate two follow-up analyses that can be performed with the resulting probability distribution. One of them is to investigate the associations among the sequence sites. This provides us a way to infer the governing biological grammar. The other is to study the global geometry of the probability landscape, which allows us to look at the problem from an evolutionary point of view. It can be seen that this methodology enables us to learn from a sample of sequences about how a biological system or phenomenon in the real world works.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
