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用于电子健康记录表型识别的贝叶斯双重特征分配AI 解读

Electronic health records (EHR) provide opportunities for deeper understanding of human phenotypes - in our case, latent disease - based on statistical modeling. We propose a categorical matrix factorization method to infer latent diseases from EHR data. A latent disease is defined as an unknown biological aberration that causes a set of common symptoms for a group of patients. The proposed approach is based on a novel double feature allocation model which simultaneously…

We propose small-variance asymptotic approximations for inference on tumor heterogeneity (TH) using next-generation sequencing data. Understanding TH is an important and open research problem in biology. The lack of appropriate statistical inference is a critical gap in existing methods that the proposed approach aims to fill. We build on a hierarchical model with an exponential family likelihood and a feature allocation prior. The proposed implementation of posterior…

基于疗效与毒性联合效用的两周期贝叶斯剂量探索AI 解读

Juhee Lee, Peter F. Thall, Yuan Ji & Peter MüllerJuhee Lee, Department of Statistics, Ohio State University, Columbus, OH 43210 (E-mail: ).Peter F. Thall, Department of Biostatistics, University of Texas, M.D. Anderson Cancer Center, Houston, TX 77230-1402 (E-mail: ).Yuan Ji, Center for Clinical and Research Informatics, North Shore University Health System, Evanston, IL 60201 (E-mail: ).Peter Müller, Department of Mathematics, University of Texas, Austin, TX 78712 (E-mail:…

用于局部聚类的非参数贝叶斯模型及其蛋白质组学应用AI 解读

We propose a nonparametric Bayesian local clustering (NoB-LoC) approach for heterogeneous data. NoB-LoC implements inference for nested clusters as posterior inference under a Bayesian model. Using protein expression data as an example, the NoB-LoC model defines a protein (column) cluster as a set of proteins that give rise to the same partition of the samples (rows). In other words, the sample partitions are nested within protein clusters. The common clustering of the…

组蛋白修饰 ChIP-Seq 数据的贝叶斯图模型AI 解读

Histone modifications (HMs) are an important post-translational feature. Different types of HMs are believed to co-exist and co-regulate biological processes such as gene expression and, therefore, are intrinsically dependent on each other. We develop inference for this complex biological network of HMs based on a graphical model using ChIP-Seq data. A critical computational hurdle in the inference for the proposed graphical model is the evaluation of a normalization…

识别阵列 CGH 数据共享拷贝数异常的贝叶斯随机分段模型AI 解读

Array-based comparative genomic hybridization (aCGH) is a high-resolution high-throughput technique for studying the genetic basis of cancer. The resulting data consists of log fluorescence ratios as a function of the genomic DNA location and provides a cytogenetic representation of the relative DNA copy number variation. Analysis of such data typically involves estimation of the underlying copy number state at each location and segmenting regions of DNA with similar copy…