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SNMNMF specifications


Unique identifier OMICS_12515
Software type Framework/Library
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Computer skills Advanced
Stability Stable
Source code URL
Maintained Yes


No version available


Publication for SNMNMF

SNMNMF citations


Gene microRNA network module analysis for ovarian cancer

BMC Syst Biol
PMCID: 5259869
PMID: 28155675
DOI: 10.1186/s12918-016-0357-1

[…] n the networks composed of both genes and miRNAs, a good module should include both genes and miRNAs with connections. To better identify the gene-miRNA modules, Zhang et al. developed a framework of SNMNMF, which is based on non-negative matrix factorization and utilized a variety of data, including gene-gene interaction (GGI) and transcription factor binding sites (TFBS) []. However, SNMNMF tend […]


Discovering MicroRNA Regulatory Modules in Multi Dimensional Cancer Genomic Data: A Survey of Computational Methods

Cancer Inform
PMCID: 5051584
PMID: 27721651
DOI: 10.4137/CIN.S39369

[…] used to identify MRM with each miR or gene permitted to be assigned to multiple MRMs. Sparsity was induced via L1 penalty, and they named their algorithm the sparse network-regularized multiple NMF (SNMNMF) technique. To guide the optimization process, the investigators incorporated sequence-based predicted target data and GGI using a semi-supervised learning framework to define constraints for M […]


Paired proteomics, transcriptomics and miRNomics in non small cell lung cancers: known and novel signaling cascades

PMCID: 5342097
PMID: 27588394
DOI: 10.18632/oncotarget.11723

[…] available a-priori information as for example predicted miRNA-mRNA interactions have been developed to address problems associated with the ab-initio network approaches []. Zhang et al. proposed the SNMNMF (Sparse Network-Regularized Multiple Non-Negative Matrix Factorization) method that incorporates miRNA and mRNA values []. In other approaches, the strength of miRNA–mRNA interactions has been […]


A survey of best practices for RNA seq data analysis

Genome Biol
PMCID: 4728800
PMID: 26813401
DOI: 10.1186/s13059-016-0881-8

[…] tion of more than two genomic data types is still at its infancy and not yet extensively applied to functional sequencing techniques, but there are already some tools that combine several data types. SNMNMF [] and PIMiM [] combine mRNA and miRNA expression data with protein–protein, DNA–protein, and miRNA–mRNA interaction networks to identify miRNA–gene regulatory modules. MONA [] combines differe […]


Connecting rules from paired miRNA and mRNA expression data sets of HCV patients to detect both inverse and positive regulatory relationships

BMC Genomics
PMCID: 4331711
PMID: 25707620
DOI: 10.1186/1471-2164-16-S2-S11

[…] re only an incomplete part of the modules in a certain biological context.Recently, Zhang et al. (2011) developed a framework of sparse network-regularized multiple non-negative matrix factorization (SNMNMF) to discover miRNA-gene comodules based on factorized coefficient matrices by integrating diverse data sources []. Le et al. (2013) designed a iterative learning framework of protein interactio […]


Identifying miRNAs, targets and functions

Brief Bioinform
PMCID: 3896928
PMID: 23175680
DOI: 10.1093/bib/bbs075

[…] to find the co-expressed miRNAs and mRNAs, then PPIs are further used to refine the structures. A novel machine learning method sparse network regularized multiple non-negative matrix factorization (SNMNMF) was developed in this work to integrate three heterogeneous data sources. They tested this method on a data set of ovarian cancer samples, and 49 significant MRMs were identified, where the mi […]


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SNMNMF institution(s)
Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA; Academy of Mathematics and Systems Science, CAS, Beijing, China; School of Computer Science, Wuhan University, Wuhan, China
SNMNMF funding source(s)
Supported by the National Institutes of Health (Grants R01GM074163); National Science Foundation (Grants 0515936; and 0747475); National Natural Science Foundation of China (No. 11001256); Innovation Project of Chinese Academy of Sciences (CAS) (kjcx-yw-s7); the Special Presidential Prize - Scientific Research Foundation of the CAS, and the Special Foundation of President of AMSS at CAS for ‘Chen Jing-Run’ Future Star Program.

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