ArticleBioinformatics (Oxford, England)2011
A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 101 papers, 1 of them a synthesis that pooled it.
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Who cites it
101 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Crucial microRNAs and genes of human primary breast cancer explored by microRNA-mRNA integrated analysis.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2015Pooled it
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- Regulatory Genetic Networks by microRNAs: Exploring Genomic Signatures in Cervical Cancer.Biomedicines · 2025Review
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- Multimodal data integration in early-stage breast cancer.Breast (Edinburgh, Scotland) · 2025Review
- sTPLS: identifying common and specific correlated patterns under multiple biological conditions.Briefings in bioinformatics · 2025Article
- NMFProfiler: a multi-omics integration method for samples stratified in groups.Bioinformatics (Oxford, England) · 2025Article
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- HetFCM: functional co-module discovery by heterogeneous network co-clustering.Nucleic acids research · 2024Article
- MOCSS: Multi-omics data clustering and cancer subtyping via shared and specific representation learning.iScience · 2023Article
- Fragmentation landscape of cell-free DNA revealed by deconvolutional analysis of end motifs.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Identification of functional gene modules by integrating multi-omics data and known molecular interactions.Frontiers in genetics · 2023Article
- A review on methods for predicting miRNA-mRNA regulatory modules.Journal of integrative bioinformatics · 2022Review
- MOMA: a multi-task attention learning algorithm for multi-omics data interpretation and classification.Bioinformatics (Oxford, England) · 2022Article
- Development and validation of a prognostic and predictive 32-gene signature for gastric cancer.Nature communications · 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
- miRModuleNet: Detecting miRNA-mRNA Regulatory Modules.Frontiers in genetics · 2022Article
- Network-based cancer genomic data integration for pattern discovery.BMC genomic data · 2021Article
41 more citing papers are in PubMed but not listed here.
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4 authors.
Funding
Abstract
motivationIt is well known that microRNAs (miRNAs) and genes work cooperatively to form the key part of gene regulatory networks. However, the specific functional roles of most miRNAs and their combinatorial effects in cellular processes are still unclear. The availability of multiple types of functional genomic data provides unprecedented opportunities to study the miRNA-gene regulation. A major challenge is how to integrate the diverse genomic data to identify the regulatory modules of miRNAs and genes.
resultsHere we propose an effective data integration framework to identify the miRNA-gene regulatory comodules. The miRNA and gene expression profiles are jointly analyzed in a multiple non-negative matrix factorization framework, and additional network data are simultaneously integrated in a regularized manner. Meanwhile, we employ the sparsity penalties to the variables to achieve modular solutions. The mathematical formulation can be effectively solved by an iterative multiplicative updating algorithm. We apply the proposed method to integrate a set of heterogeneous data sources including the expression profiles of miRNAs and genes on 385 human ovarian cancer samples, computationally predicted miRNA-gene interactions, and gene-gene interactions. We demonstrate that the miRNAs and genes in 69% of the regulatory comodules are significantly associated. Moreover, the comodules are significantly enriched in known functional sets such as miRNA clusters, GO biological processes and KEGG pathways, respectively. Furthermore, many miRNAs and genes in the comodules are related with various cancers including ovarian cancer. Finally, we show that comodules can stratify patients (samples) into groups with significant clinical characteristics. AVAILABILITY: The program and supplementary materials are available at http://zhoulab.usc.edu/SNMNMF/. CONTACT: xjzhou@usc.edu; zsh@amss.ac.cn
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