49 citations · 181 across the 10 of their papers we have counts for
10 papers
A Semi-parametric Bayesian Approach to Population Finding with Time-to-Event and Toxicity Data in a Randomized Clinical Trial
Satoshi Morita, Peter Müller, Hiroyasu Abe
A utility-based Bayesian population finding (BaPoFi) method was proposed by Morita and Müller (2017, Biometrics, 1355-1365) to analyze data from a randomized clinical trial with th…
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…
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…
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…