ArticleIEEE/ACM transactions on computational biology and bioinformatics
Integration of Imaging (epi)Genomics Data for the Study of Schizophrenia Using Group Sparse Joint Nonnegative Matrix Factorization.
Article in IEEE/ACM transactions on computational biology and bioinformatics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Exploring Brain Imaging and Genetic Risk Factors in Different Progression States of Alzheimer's Disease Through OSnetNMF-Based Methods.Journal of molecular neuroscience : MN · 2025Article
- Multi-Modal Imaging Genetics Data Fusion via a Hypergraph-Based Manifold Regularization: Application to Schizophrenia Study.IEEE transactions on medical imaging · 2022Article
- Multiscale Weighted Permutation Entropy Analysis of Schizophrenia Magnetoencephalograms.Entropy (Basel, Switzerland) · 2022Article
- Integration of Imaging Genomics Data for the Study of Alzheimer's Disease Using Joint-Connectivity-Based Sparse Nonnegative Matrix Factorization.Journal of molecular neuroscience : MN · 2022Article
- Multi-modal intermediate integrative methods in neuropsychiatric disorders: A review.Computational and structural biotechnology journal · 2022Review
- Biomarker Identification Through Integrating fMRI and Epigenetics.IEEE transactions on bio-medical engineering · 2020Article
- Brain Imaging Genomics: Integrated Analysis and Machine Learning.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2020Article
- Group Sparse Joint Non-Negative Matrix Factorization on Orthogonal Subspace for Multi-Modal Imaging Genetics Data Analysis.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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Authors and funding
5 authors.
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Abstract
Schizophrenia (SZ) is a complex disease. Single nucleotide polymorphism (SNP), brain activity measured by functional magnetic resonance imaging (fMRI) and DNA methylation are all important biomarkers that can be used for the study of SZ. To our knowledge, there has been little effort to combine these three datasets together. In this study, we propose a group sparse joint nonnegative matrix factorization (GSJNMF) model to integrate SNP, fMRI, and DNA methylation for the identification of multi-dimensional modules associated with SZ, which can be used to study regulatory mechanisms underlying SZ at multiple levels. The proposed GSJNMF model projects multiple types of data onto a common feature space, in which heterogeneous variables with large coefficients on the same projected bases are used to identify multi-dimensional modules. We also incorporate group structure information available from each dataset. The genomic factors in such modules have significant correlations or functional associations with several brain activities. At the end, we have applied the method to the analysis of real data collected from the Mind Clinical Imaging Consortium (MCIC) for the study of SZ and identified significant biomarkers. These biomarkers were further used to discover genes and corresponding brain regions, which were confirmed to be significantly associated with SZ.
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