<i>RFCM<sup>3</sup>
                  </i>: Computational Method for Identification of miRNA-mRNA Regulatory Modules in Cervical Cancer

Paul, Sushmita; Madhumita,

doi:10.1109/tcbb.2019.2910851

Cited by 10 publications

(5 citation statements)

References 46 publications

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“…In order to detect the miRNA-mRNA regulatory modules, we have used the RFCM 3 approach suggested by ( Paul and Madhumita, 2020 ). The RFCM 3 considers two types of -omics data, the miRNA and mRNA expression profiles from the same samples.…”

Section: Methodsmentioning

confidence: 99%

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miRModuleNet: Detecting miRNA-mRNA Regulatory Modules

2022

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Increasing evidence that microRNAs (miRNAs) play a key role in carcinogenesis has revealed the need for elucidating the mechanisms of miRNA regulation and the roles of miRNAs in gene-regulatory networks. A better understanding of the interactions between miRNAs and their mRNA targets will provide a better understanding of the complex biological processes that occur during carcinogenesis. Increased efforts to reveal these interactions have led to the development of a variety of tools to detect and understand these interactions. We have recently described a machine learning approach miRcorrNet, based on grouping and scoring (ranking) groups of genes, where each group is associated with a miRNA and the group members are genes with expression patterns that are correlated with this specific miRNA. The miRcorrNet tool requires two types of -omics data, miRNA and mRNA expression profiles, as an input file. In this study we describe miRModuleNet, which groups mRNA (genes) that are correlated with each miRNA to form a star shape, which we identify as a miRNA-mRNA regulatory module. A scoring procedure is then applied to each module to further assess their contribution in terms of classification. An important output of miRModuleNet is that it provides a hierarchical list of significant miRNA-mRNA regulatory modules. miRModuleNet was further validated on external datasets for their disease associations, and functional enrichment analysis was also performed. The application of miRModuleNet aids the identification of functional relationships between significant biomarkers and reveals essential pathways involved in cancer pathogenesis. The miRModuleNet tool and all other supplementary files are available at https://github.com/malikyousef/miRModuleNet/

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Section: Methodsmentioning

confidence: 99%

“…As illustrated in the Step 5 of Figure 4 , we have generated the module called the star shaped module, where it contains a single miRNA and multiple mRNAs/genes. As suggested by ( Paul and Madhumita, 2020 ), mRNAs for such modules are selected in such a way that they are simultaneously and functionally similar to the corresponding miRNA.…”

Section: Methodsmentioning

confidence: 99%

miRModuleNet: Detecting miRNA-mRNA Regulatory Modules

2022

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“…Relevant and Functionally Consistent miRNA–mRNA Modules (RFCM 3 ) [ 33 ] uses Mutual Information (MI) to identify regulatory modules containing multiple biomarkers of both types. A two-stage approach, which is used to derive relevant and functionally consistent MRMs are described further.…”

Section: Computational Approaches For Mrms Identificationmentioning

confidence: 99%

“…Therefore, users have the opportunity to mix and match these steps based on data availability and the mathematical model they want to use. Suppose a user wants to calculate the association between miRNAs and mRNAs using MI and not correlation and also have no MISIM [ 70 ] information for generating final MRMs as par the RFCM 3 [ 33 ] method. He/She can use the concept of synergy score used in Mirsynergy [ 12 ] and compute the association between multiple miRNAs.…”

Section: Recommendation For the Usersmentioning

confidence: 99%

A review on methods for predicting miRNA–mRNA regulatory modules

Madhumita

Paul

2022

Journal of Integrative Bioinformatics

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Identification of complex interactions between miRNAs and mRNAs in a regulatory network helps better understand the underlying biological processes. Previously, identification of these interactions was based on sequence-based predicted target binding information. With the advancement in high-throughput omics technologies, miRNA and mRNA expression for the same set of samples are available. This helps develop more efficient and flexible approaches that work by integrating miRNA and mRNA expression profiles with target binding information. Since these integrative approaches of miRNA–mRNA regulatory modules (MRMs) detection is sufficiently able to capture the minute biological details, 26 such algorithms/methods/tools for MRMs identification are comprehensively reviewed in this article. The study covers the significant features underlying every method. Therefore, the methods are classified into eight groups based on mathematical approaches to understand their working and suitability for one’s study. An algorithm could be selected based on the available information with the users and the biological question under investigation.

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“…Apart from these methods, other proposed algorithms have been proposed to identify cooperative miRNAs that focus on identifying cooperative modules. RFCM3 [8] identifies functional miRNA-mRNA modules by maximizing the relation of mRNAs with miRNAs and functional similarity between selected mRNAs. Similarly, ProModule [9] detects miRNA prognostic modules.…”

Section: Introductionmentioning

confidence: 99%

miRCoop: Identifying Cooperating miRNAs via Kernel Based Interaction Tests

Olgun¹,

Taştan²

2022

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Although miRNAs can cause widespread changes in expression programs, single miRNAs typically induce mild repression on their targets. Cooperativity among miRNAs is reported as one strategy to overcome this constraint. Expanding the catalog of synergistic miRNAs is critical for understanding gene regulation and for developing miRNA-based therapeutics. In this study, we develop miRCoop to identify synergistic miRNA pairs that have weak or no repression on the target mRNA individually, but when act together, induce strong repression. miRCoop uses kernel-based statistical interaction tests, together with miRNA and mRNA target information. We apply our approach to patient data of two different cancer types. In kidney cancer, we identify 66 putative triplets. For 64 of these triplets, there is at least one common transcription factor that potentially regulates all participating RNAs of the triplet, supporting a functional association among them. Furthermore, we find that identified triplets are enriched for certain biological processes that are relevant to kidney cancer. Some of the synergistic miRNAs are very closely encoded in the genome, hinting a functional association among them. In applying the method on tumor data with the primary liver site, we find 3105 potential triplet interactions. We believe miRCoop can aid our understanding of the complex regulatory interactions in different health and disease states of the cell and can help in designing miRNA-based therapies. Matlab code for the methodology is provided in https://github.com/guldenolgun/miRCoop.

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RFCM³ : Computational Method for Identification of miRNA-mRNA Regulatory Modules in Cervical Cancer

Cited by 10 publications

References 46 publications

miRModuleNet: Detecting miRNA-mRNA Regulatory Modules

miRModuleNet: Detecting miRNA-mRNA Regulatory Modules

A review on methods for predicting miRNA–mRNA regulatory modules

miRCoop: Identifying Cooperating miRNAs via Kernel Based Interaction Tests

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RFCM3 : Computational Method for Identification of miRNA-mRNA Regulatory Modules in Cervical Cancer

Cited by 10 publications

References 46 publications

miRModuleNet: Detecting miRNA-mRNA Regulatory Modules

miRModuleNet: Detecting miRNA-mRNA Regulatory Modules

A review on methods for predicting miRNA–mRNA regulatory modules

miRCoop: Identifying Cooperating miRNAs via Kernel Based Interaction Tests

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Product

Resources

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RFCM³ : Computational Method for Identification of miRNA-mRNA Regulatory Modules in Cervical Cancer