42 citations · 159 across the 15 of their papers we have counts for
16 papers
Semi-Parametric Bayesian Additive Regression Trees for Risk Prediction with High-Dimensional Epigenetic Signatures and Low-Dimensional Covariates
Saurabh Bhandari, Parveen Bhatti, Brian C. -H. Chiu +1
In the era of precision medicine, genome-wide epigenetic modifications offer rich data that could inform risk prediction. However, these data are high-dimensional and exhibit compl…
A Class of Dependent Random Distributions Based on Atom Skipping
Dehua Bi, Yuan Ji
We propose the Plaid Atoms Model (PAM), a novel Bayesian nonparametric model for grouped data. Founded on an idea of `atom skipping', PAM is part of a well-established category of…
Consensus Monte Carlo for Random Subsets using Shared Anchors
Yang Ni, Yuan Ji, Peter Mueller
We present a consensus Monte Carlo algorithm that scales existing Bayesian nonparametric models for clustering and feature allocation to big data. The algorithm is valid for any pr…
Bayesian Double Feature Allocation for Phenotyping with Electronic Health Records
Yang Ni, Peter Mueller, Yuan Ji
We propose a categorical matrix factorization method to infer latent diseases from electronic health records (EHR) data in an unsupervised manner. A latent disease is defined as an…
Scalable Bayesian Nonparametric Clustering and Classification
Yang Ni, Peter Müller, Maurice Diesendruck +3
We develop a scalable multi-step Monte Carlo algorithm for inference under a large class of nonparametric Bayesian models for clustering and classification. Each step is "embarrass…
A Bayesian Mixture Model for Clustering on the Stiefel Manifold
Subhajit Sengupta, Subhadip Pal, Riten Mitra +3
Analysis of a Bayesian mixture model for the Matrix Langevin distribution on the Stiefel manifold is presented. The model exploits a particular parametrization of the Matrix Langev…
TreeClone: Reconstruction of Tumor Subclone Phylogeny Based on Mutation Pairs using Next Generation Sequencing Data
Tianjian Zhou, Subhajit Sengupta, Peter Mueller +1
We present TreeClone, a latent feature allocation model to reconstruct tumor subclones subject to phylogenetic evolution that mimics tumor evolution. Similar to most current method…
PairClone: A Bayesian Subclone Caller Based on Mutation Pairs
Tianjian Zhou, Peter Mueller, Subhajit Sengupta +1
Tumor cell populations can be thought of as being composed of homogeneous cell subpopulations, with each subpopulation being characterized by overlapping sets of single nucleotide…
Heterogeneous Reciprocal Graphical Models
Yang Ni, Peter Mueller, Yitan Zhu +1
We develop novel hierarchical reciprocal graphical models to infer gene networks from heterogeneous data. In the case of data that can be naturally divided into known groups, we pr…
Reciprocal Graphical Models for Integrative Gene Regulatory Network Analysis
Yang Ni, Yuan Ji, Peter Mueller
Constructing gene regulatory networks is a fundamental task in systems biology. We introduce a Gaussian reciprocal graphical model for inference about gene regulatory relationships…
A Bayesian feature allocation model for tumor heterogeneity
Juhee Lee, Peter Müller, Kamalakar Gulukota +1
We develop a feature allocation model for inference on genetic tumor variation using next-generation sequencing data. Specifically, we record single nucleotide variants (SNVs) base…
Bayesian Inference for Tumor Subclones Accounting for Sequencing and Structural Variants
Juhee Lee, Peter Mueller, Subhajit Sengupta +2
Tumor samples are heterogeneous. They consist of different subclones that are characterized by differences in DNA nucleotide sequences and copy numbers on multiple loci. Heterogene…
A Latent Gaussian Process Model with Application to Monitoring Clinical Trials
Yanxun Xu, Yuan Ji
In many clinical trials treatments need to be repeatedly applied as diseases relapse frequently after remission over a long period of time (e.g., 35 weeks). Most research in statis…
Bayesian sparse graphical models for classification with application to protein expression data
Veerabhadran Baladandayuthapani, Rajesh Talluri, Yuan Ji +5
Reverse-phase protein array (RPPA) analysis is a powerful, relatively new platform that allows for high-throughput, quantitative analysis of protein networks. One of the challenges…
Subgroup-Based Adaptive (SUBA) Designs for Multi-Arm Biomarker Trials
Yanxun Xu, Lorenzo Trippa, Peter Müller +1
Targeted therapies based on biomarker profiling are becoming a mainstream direction of cancer research and treatment. Depending on the expression of specific prognostic biomarkers,…
MAD Bayes for Tumor Heterogeneity Feature Allocation with Non-Normal Sampling
Yanxun Xu, Peter Mueller, Yuan Yuan +2
We propose small-variance asymptotic approximations for the inference of tumor heterogeneity (TH) using next-generation sequencing data. Understanding TH is an important and open r…