ArticleBMC bioinformatics2014
Structure-revealing data fusion.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Spectroscopic technologies and data fusion: Applications for the dairy industry.Frontiers in nutrition · 2022Pooled it
- Tensor-based integration of time-series measurements reveals relationships between underlying disease, early injury, and T cell polarization in liver transplantation.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2026Article
- ACMTF-R: Supervised multi-omics data integration uncovering shared and distinct outcome-associated variation.PloS one · 2026Article
- A Roadmap for the Future of Systems Biology in Cancer Research.Cancer research · 2025Review
- A generalized higher-order correlation analysis framework for multi-omics network inference.PLoS computational biology · 2025Article
- MiMeJF: Application of Coupled Matrix and Tensor Factorization (CMTF) for Enhanced Microbiome-Metabolome Multi-Omic Analysis.Metabolites · 2025Article
- Longitudinal Metabolomics Data Analysis Informed by Mechanistic Models.Metabolites · 2024Article
- Systems profiling reveals recurrently dysregulated cytokine signaling responses in ER+ breast cancer patients' blood.NPJ systems biology and applications · 2024Article
- Multi-way modelling of oral microbial dynamics and host-microbiome interactions during induced gingivitis.NPJ biofilms and microbiomes · 2024Article
- Learning Spatiotemporal Brain Dynamics in Adolescents via Multimodal MEG and fMRI Data Fusion Using Joint Tensor/Matrix Decomposition.IEEE transactions on bio-medical engineering · 2024Article
- Tensor modeling of MRSA bacteremia cytokine and transcriptional patterns reveals coordinated, outcome-associated immunological programs.PNAS nexus · 2024Article
- Systems profiling reveals recurrently dysregulated cytokine signaling responses in ER+ breast cancer patients' blood.bioRxiv : the preprint server for biology · 2023Article
- Exploring Neural Mechanisms of Reward Processing Using Coupled Matrix Tensor Factorization: A Simultaneous EEG-fMRI Investigation.Brain sciences · 2023Article
- New Constructed EEM Spectra Combined with N-PLS Analysis Approach as an Effective Way to Determine Multiple Target Compounds in Complex Samples.Molecules (Basel, Switzerland) · 2022Article
- Dynamic functional connectivity estimation for neurofeedback emotion regulation paradigm with simultaneous EEG-fMRI analysis.Frontiers in human neuroscience · 2022Article
- Augmenting interictal mapping with neurovascular coupling biomarkers by structured factorization of epileptic EEG and fMRI data.NeuroImage · 2021Article
- Computational Methods for Single-Cell Imaging and Omics Data Integration.Frontiers in molecular biosciences · 2021Review
- Unraveling Diagnostic Biomarkers of Schizophrenia Through Structure-Revealing Fusion of Multi-Modal Neuroimaging Data.Frontiers in neuroscience · 2019Article
- The GNAT: A new tool for processing NMR data.Magnetic resonance in chemistry : MRC · 2018Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAnalysis of data from multiple sources has the potential to enhance knowledge discovery by capturing underlying structures, which are, otherwise, difficult to extract. Fusing data from multiple sources has already proved useful in many applications in social network analysis, signal processing and bioinformatics. However, data fusion is challenging since data from multiple sources are often (i) heterogeneous (i.e., in the form of higher-order tensors and matrices), (ii) incomplete, and (iii) have both shared and unshared components. In order to address these challenges, in this paper, we introduce a novel unsupervised data fusion model based on joint factorization of matrices and higher-order tensors.
resultsWhile the traditional formulation of coupled matrix and tensor factorizations modeling only shared factors fails to capture the underlying structures in the presence of both shared and unshared factors, the proposed data fusion model has the potential to automatically reveal shared and unshared components through modeling constraints. Using numerical experiments, we demonstrate the effectiveness of the proposed approach in terms of identifying shared and unshared components. Furthermore, we measure a set of mixtures with known chemical composition using both LC-MS (Liquid Chromatography - Mass Spectrometry) and NMR (Nuclear Magnetic Resonance) and demonstrate that the structure-revealing data fusion model can (i) successfully capture the chemicals in the mixtures and extract the relative concentrations of the chemicals accurately, (ii) provide promising results in terms of identifying shared and unshared chemicals, and (iii) reveal the relevant patterns in LC-MS by coupling with the diffusion NMR data.
conclusionsWe have proposed a structure-revealing data fusion model that can jointly analyze heterogeneous, incomplete data sets with shared and unshared components and demonstrated its promising performance as well as potential limitations on both simulated and real data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.