Journal ArticleDOI
RFCM 3 : Computational Method for Identification of miRNA-mRNA Regulatory Modules in Cervical Cancer
Sushmita Paul,Madhumita +1 more
TLDR
The proposed algorithm, termed as relevant and functionally consistent miRNA-mRNA modules (RFCM3), is found to generate more robust, integrated, and functionally enriched mi RNA-m RNA modules in cervical cancer.Abstract:
Cervical cancer is a leading severe malignancy throughout the world. Molecular processes and biomarkers leading to tumor progression in cervical cancer are either unknown or only partially understood. An increasing number of studies have shown that microRNAs play an important role in tumorigenesis so understanding the regulatory mechanism of miRNAs in gene-regulatory network will help elucidate the complex biological processes that occur during malignancy. Functional genomics data provides opportunities to study the aberrant microRNA-messenger RNA (miRNA-mRNA) interaction. Identification of miRNA-mRNA regulatory modules will aid deciphering aberrant transcriptional regulatory network in cervical cancer but is computationally challenging. In this regard, an algorithm, termed as relevant and functionally consistent miRNA-mRNA modules (RFCM3), is proposed. It integrates miRNA and mRNA expression data of cervical cancer for identification of potential miRNA-mRNA modules. It selects set of miRNA-mRNA modules by maximizing relation of mRNAs with miRNA and functional similarity between selected mRNAs. Later, using the knowledge of the miRNA-miRNA synergistic network different modules are fused and finally a set of modules are generated containing several miRNAs as well as mRNAs. This type of module explains the underlying biological pathways containing multiple miRNAs and mRNAs. The effectiveness of the proposed approach over other existing methods has been demonstrated on a miRNA and mRNA expression data of cervical cancer with respect to enrichment analyses and other standard metrices. The prognostic value of the genes in a module with respect to cervical cancer is also demonstrated. The approach was found to generate more robust, integrated, and functionally enriched miRNA-mRNA modules in cervical cancer.read more
Citations
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MicroRNAs: Target Recognition and Regulatory Functions
TL;DR: In this article, a review outlines the current understanding of miRNA target recognition in animals and discusses the widespread impact of miRNAs on both the expression and evolution of protein-coding genes.
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miRModuleNet: Detecting miRNA-mRNA Regulatory Modules
TL;DR:
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Deciphering microRNA-mRNA regulatory network in adult T-cell leukemia/lymphoma; the battle between oncogenes and anti-oncogenes.
TL;DR: In this paper, the potential regulatory subnetworks were constructed aiming to shed light on the pathogenesis mechanism of adult T-cell leukemia/lymphoma (ATLL) through dysexpression of miRNAs and mRNAs.
Journal ArticleDOI
Capturing the latent space of an Autoencoder for multi-omics integration and cancer subtyping
Madhumita,Sushmita Paul +1 more
TL;DR: In this paper , an AE-assisted cancer subtyping framework is presented that utilizes the compressed latent space of a Sparse AE neural network for multi-omics clustering, where the selected features from each of the omic data are passed to the AE.
Journal ArticleDOI
A review on methods for predicting miRNA–mRNA regulatory modules
TL;DR: 26 algorithms/methods/tools for MRMs identification are comprehensively reviewed and they are classified into eight groups based on mathematical approaches to understand their working and suitability for one’s study.
References
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