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Found 14 papers, 14 papers with code
Multiple Augmented Reduced Rank Regression for Pan-Cancer Analysis
To address variation architectures correctly and comprehensively for high-dimensional data across multiple sample sets (i. e., cohorts), we propose multiple augmented reduced rank regression (maRRR), a flexible matrix regression and factorization method to concurrently learn both covariate-driven and auxiliary structured variation.
Bayesian Simultaneous Factorization and Prediction Using Multi-Omic Data
We show via simulation that BSFP is competitive in recovering latent variation structure, as well as the importance of propagating uncertainty from the estimated factorization to prediction.
Bayesian predictive modeling of multi-source multi-way data
We develop a Bayesian approach to predict a continuous or binary outcome from data that are collected from multiple sources with a multi-way (i. e.. multidimensional tensor) structure.
Multiway sparse distance weighted discrimination
We conducted extensive simulation studies, showing that our model is robust to the degree of sparsity and improves classification accuracy when the data have multiway structure.
sJIVE: Supervised Joint and Individual Variation Explained
We propose a method called supervised joint and individual variation explained (sJIVE) that can simultaneously (1) identify shared (joint) and source-specific (individual) underlying structure and (2) build a linear prediction model for an outcome using these structures.
Bayesian Distance Weighted Discrimination
In this article we show that DWD identifies the mode of a proper Bayesian posterior distribution, that results from a particular link function for the class probabilities and a shrinkage-inducing proper prior distribution on the coefficients.
Bidimensional linked matrix factorization for pan-omics pan-cancer analysis
This builds on a growing literature for the factorization and decomposition of linked matrices, which has primarily focused on multiple matrices that are linked in one dimension (rows or columns) only.
Integrative Factorization of Bidimensionally Linked Matrices
This is limiting for data that take the form of bidimensionally linked matrices (e. g., multiple cohorts measured on multiple platforms), which are increasingly common in large-scale biomedical studies.
Bayesian nonparametric multiway regression for clustered binomial data
We introduce a Bayesian nonparametric regression model for data with multiway (tensor) structure, motivated by an application to periodontal disease (PD) data.
Detecting Multiple Random Changepoints in Bayesian Piecewise Growth Mixture Models
A random changepoint allows for individual differences in the transition time within each class.
Methodology
Tensor-on-tensor regression
We propose a framework for the linear prediction of a multi-way array (i. e., a tensor) from another multi-way array of arbitrary dimension, using the contracted tensor product.
Supervised multiway factorization
We describe a likelihood-based latent variable representation of the CP factorization, in which the latent variables are informed by additional covariates.
Discriminating sample groups with multi-way data
However, their use is limited to applications where a single vector of features is measured for each subject.
Joint and individual variation explained (JIVE) for integrated analysis of multiple data types
In this paper we introduce Joint and Individual Variation Explained (JIVE), a general decomposition of variation for the integrated analysis of such data sets.
