Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks

Wang, Peizhuo; Gao, Lin; Hu, Yuxuan; Li, Feng

doi:10.1186/s12859-018-2434-5

Cited by 15 publications

(16 citation statements)

References 52 publications

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“…The second stage of our NDM method is to exploit potential associations between proteins from the above reliable weighted network using the Non-negative matrix factorization (NMF) technology. As an effective data representation technique, the NMF has been widely used in lncRNA-disease associations prediction [25], conserved functional modules detection [26], etc. For our purpose, we represent the reliable weighted network constructed in the first stage as an adjacency matrix ∈ × .…”

Section: Reconstruction Of a Comprehensive Protein Interactome Network Based On Nmfmentioning

confidence: 99%

A Non-negative Matrix Factorization Based Method for Identifying Essential Proteins

Hu¹,

Yan²,

Zhao³

et al. 2021

Preprint

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BackgroundIdentification of essential proteins is very important for understanding the basic requirements to sustain a living organism. In recent years, various different computational methods have been proposed to identify essential proteins based on protein-protein interaction (PPI) networks. However, there has been reliable evidence that a huge amount of false negatives and false positives exist in PPI data. Therefore, it is necessary to reduce the influence of false data on accuracy of essential proteins prediction by integrating multi-source biological information with PPI networks.ResultsIn this paper, we proposed a non-negative matrix factorization and multiple biological information based model (NDM) for identifying essential proteins. The first stage in this progress was to construct a weighted PPI network by combing the information of protein domain, protein complex and the topology characteristic of the original PPI network. Then, the non-negative matrix factorization technique was used to reconstruct an optimized PPI network with whole enough weight of edges. In the final stage, the ranking score of each protein was computed by the PageRank algorithm in which the initial scores were calculated with homologous and subcellular localization information. In order to verify the effectiveness of the NDM method, we compared the NDM with other state-of-the-art essential proteins prediction methods. The comparison of the results obtained from different methods indicated that our NDM model has better performance in predicting essential proteins.ConclusionEmploying the non-negative matrix factorization and integrating multi-source biological data can effectively improve quality of the PPI network, which resulted in the led to optimization of the performance essential proteins identification. This will also provide a new perspective for other prediction based on protein-protein interaction networks.

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Section: Reconstruction Of a Comprehensive Protein Interactome Network Based On Nmfmentioning

confidence: 99%

A Non-negative Matrix Factorization Based Method for Identifying Essential Proteins

Hu¹,

Yan²,

Zhao³

et al. 2021

Preprint

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“…It is often aimed for a comparison of more than two networks simultaneously, such as gene co-expression networks arising from different species, tissues or diseases, or co-existence networks from different environments. Existing methods for contrasting multiple networks focus on identifying modules of differentially co-expressed genes [ 1 , 20 , 23 , 34 – 36 ], thereby allowing the identification of gene groups that are functionally related. Note that, such module-centric analyses do not enable a straightforward identification of which links have changed between networks or which nodes are most differentially connected in the networks.…”

Section: Introductionmentioning

confidence: 99%

“…However, the focus of CompNet is on the visualization of the union, intersections and exclusive links of the analyzed networks. ConMOd [ 34 ] has recently been developed to find conserved functional modules across multiple biological networks. Another method, DCEA [ 35 ], measures the average co-expression difference in each gene, resulting in Differentially Coexpressed Genes, and subsequently infers the enrichment of links for each gene (Differentially Coexpressed Links).…”

Section: Introductionmentioning

confidence: 99%

Whole transcriptomic network analysis using Co-expression Differential Network Analysis (CoDiNA)

et al. 2020

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Biological and medical sciences are increasingly acknowledging the significance of gene co-expression-networks for investigating complex-systems, phenotypes or diseases. Typically, complex phenotypes are investigated under varying conditions. While approaches for comparing nodes and links in two networks exist, almost no methods for the comparison of multiple networks are available and—to best of our knowledge—no comparative method allows for whole transcriptomic network analysis. However, it is the aim of many studies to compare networks of different conditions, for example, tissues, diseases, treatments, time points, or species. Here we present a method for the systematic comparison of an unlimited number of networks, with unlimited number of transcripts: Co -expression Di fferential N etwork A nalysis (CoDiNA). In particular, CoDiNA detects links and nodes that are common, specific or different among the networks. We developed a statistical framework to normalize between these different categories of common or changed network links and nodes, resulting in a comprehensive network analysis method, more sophisticated than simply comparing the presence or absence of network nodes. Applying CoDiNA to a neurogenesis study we identified candidate genes involved in neuronal differentiation. We experimentally validated one candidate, demonstrating that its overexpression resulted in a significant disturbance in the underlying gene regulatory network of neurogenesis. Using clinical studies, we compared whole transcriptome co-expression networks from individuals with or without HIV and active tuberculosis (TB) and detected signature genes specific to HIV. Furthermore, analyzing multiple cancer transcription factor (TF) networks, we identified common and distinct features for particular cancer types. These CoDiNA applications demonstrate the successful detection of genes associated with specific phenotypes. Moreover, CoDiNA can also be used for comparing other types of undirected networks, for example, metabolic, protein-protein interaction, ecological and psychometric networks. CoDiNA is publicly available as an package in CRAN ( https://CRAN.R-project.org/package=CoDiNA ).

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“…Valdeolivas et al (2019) extended the Random walk algorithm to multiplex networks by building an nL × nL heterogeneous matrix in which n and L represent the number of nodes and layers of the multiplex network, respectively. Wang et al (2018) compressed the multiple networks into two feature matrices and performed conserved functional modules detection by multi-view nonnegative matrix factorization. In a newly proposed link prediction algorithm (Samei and Jalili, 2019) for multiplex networks, both intra-layer information and inter-layer information are combined based on layer relevance.…”

Section: Introductionmentioning

confidence: 99%

A Novel Computational Approach for Identifying Essential Proteins From Multiplex Biological Networks

Zhao

Liu

et al. 2020

Front. Genet.

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The identification of essential proteins can help in understanding the minimum requirements for cell survival and development. Ever-increasing amounts of high-throughput data provide us with opportunities to detect essential proteins from protein interaction networks (PINs). Existing network-based approaches are limited by the poor quality of the underlying PIN data, which exhibits high rates of false positive and false negative results. To overcome this problem, researchers have focused on the prediction of essential proteins by combining PINs with other biological data, which has led to the emergence of various interactions between proteins. It remains challenging, however, to use aggregated multiplex interactions within a single analysis framework to identify essential proteins. In this study, we created a multiplex biological network (MON) by initially integrating PINs, protein domains, and gene expression profiles. Next, we proposed a new approach to discover essential proteins by extending the random walk with restart algorithm to the tensor, which provides a data model representation of the MON. In contrast to existing approaches, the proposed MON approach considers for the importance of nodes and the different types of interactions between proteins during the iteration. MON was implemented to identify essential proteins within two yeast PINs. Our comprehensive experimental results demonstrated that MON outperformed 11 other state-of-the-art approaches in terms of precision-recall curve, jackknife curve, and other criteria.

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Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks

Cited by 15 publications

References 52 publications

A Non-negative Matrix Factorization Based Method for Identifying Essential Proteins

A Non-negative Matrix Factorization Based Method for Identifying Essential Proteins

Whole transcriptomic network analysis using Co-expression Differential Network Analysis (CoDiNA)

A Novel Computational Approach for Identifying Essential Proteins From Multiplex Biological Networks

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