Pattern Identification in Sequence Activity Data

在数据中发现模式是信息科学中最具挑战性的开放问题之一。可能的关系的数量与数据集的大小相结合地扩展，压倒了计算资源可用性的指数增长。物理洞察力在开发高效的计算启发式方法方面发挥了重要作用。我们用量子场论的方法，反思了三个世纪以来的贝叶斯推理，从寻找模式景观的角度阐述了这个问题，并准确地解决了这个问题。 我们的演算的一般性通过将其应用于手写数字图像和从序列比对中找到蛋白质的结构特征来说明，而不需要任何关于适合特定数据集的模型先验的假设。我们正在将这一微积分应用于除蛋白质结构之外的几个问题：(1)根据CAGE-SEQ数据在基因组规模上识别转录起始点；(2)共生细菌相互作用；(3)根据时间进程数据识别动态系统；(4)直接从大规模平行报告分析计算生物物理定量序列活动模型；(5)直接从表达数据推导出相互作用图；(6)从全基因组关联研究中直接计算SNP相互作用。

Finding patterns in data is one of the most challenging open questions in infor- mation science. The number of possible relationships scales combinatorially with the size of the dataset, overwhelming the exponential increase in avail- ability of computational resources. Physical insights have been instrumental in developing efficient computational heuristics. Using quantum field theory methods and rethinking three centuries of Bayesian inference, we formulated the problem in terms of finding landscapes of patterns and solved this problem exactly. The generality of our calculus is illustrated by applying it to handwritten digit images and to finding structural features in proteins from sequence alignments without any presumptions about model priors suited to specific datasets. We are applying this calculus to several problems besides protein structure: (1) Transcription start site identification at a genome scale from CAGE-seq data; (2) Commensal bacteria interactions; (3) Identifying dynamical systems from time-course data; (4) Calculating biophysical quantitative sequence activity models directly from massively parallel reporter assays without optimization; (5) Deriving the graph of interactions directly from expression data; (6) Direct computation of SNP interactions from genome-wide association studies.