Tensor decomposition and applications

multiTEMPTED: Joint Dimensionality Reduction of Longitudinal Multi-omic Data with Modality-Specific Temporal Dynamics

multiTEMPTED jointly reduces dimensionality of longitudinal multi-omic data, capturing modality-specific temporal dynamics and outperforms existing methods in speed and accuracy.

Assessing tensor decomposition quality of immune profiling data from a dictionary learning perspective

The paper introduces metrics for assessing tensor decomposition quality in immune profiling data using a dictionary learning framework.

tensorOmics: Data integration for longitudinal omics data using tensor factorisation

tensorOmics integrates longitudinal multi-omics data using tensor factorisation to capture coordinated molecular responses across time and omic layers.

Unified Analysis of Multi-order Tensors for Integrative Molecular Profiling

The paper introduces MUSIC, a Bayesian tensor decomposition framework for integrating multi-modal molecular data, showing it reveals interpretable patterns across patients, cell types, and modalities.

Censored Least Squares for Imputing Missing Values in PARAFAC Tensor Factorization

This study compares censored least squares to other methods for imputing missing values in tensor factorization of biological datasets, finding censored least squares to be more accurate and efficient.

Four-Dimensional Sparse Bayesian Tensor Decomposition for Gene Expression Data

The paper introduces a 4D tensor decomposition method for gene expression data, showing improved structure estimation compared to 3D methods on simulated data.

Combining tensor decomposition and time warping models for multi-neuronal spike train analysis

This paper combines tensor decomposition and time warping models to improve analysis of multi-neuronal spike trains by addressing time shifts and uncovering hidden structure in simulated data.

Multiomics data analysis using tensor decomposition based unsupervised feature extraction --Comparison with DIABLO--

The paper proposes tensor decomposition for multiomics data integration and compares it to DIABLO, showing successful integration of miRNA, mRNA, and proteome data.

Computational Disease Subtyping based on Joint Analysis of Clinical and Genomic Data

This paper uses non-negative tensor decomposition to discover breast cancer subtypes from clinical and genomic data, finding that the resulting subtypes predict patient survival better than existing methods.

Three-way clustering of multi-tissue multi-individual gene expression data using constrained tensor decomposition

The paper introduces MultiCluster, a tensor decomposition method for analyzing multi-tissue gene expression data, showing improved accuracy and speed over existing methods.

tensorBF: an R package for Bayesian tensor factorization

The paper introduces tensorBF, an R package for Bayesian tensor factorization, and demonstrates its application to drug response data.