MMiRNA-Viewer2, a bioinformatics tool for visualizing functional annotation for MiRNA and MRNA pairs in a network

Bai, Yongsheng; Baker, Steve; Exoo, Kevin; Dai, Xingqin; Ding, Lizhong; Khattak, Naureen Aslam; Li, Hongtao; Liu, Hannah; Liu, Xiaoming

doi:10.1186/s12859-020-3436-7

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…Biclustering solutions can be represented using graphs with observations and attributes represented as nodes [ 158 , 167 , 168 ]. Biclusters are then represented either by labeled edges in a graph ( Figure 22 ) or by a shaded area ( Figure 23 ).…”

Section: Evaluation Measuresmentioning

confidence: 99%

Biclustering data analysis: a comprehensive survey

Castanho,

Aidos,

Madeira

2024

Briefings in Bioinformatics

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Biclustering, the simultaneous clustering of rows and columns of a data matrix, has proved its effectiveness in bioinformatics due to its capacity to produce local instead of global models, evolving from a key technique used in gene expression data analysis into one of the most used approaches for pattern discovery and identification of biological modules, used in both descriptive and predictive learning tasks. This survey presents a comprehensive overview of biclustering. It proposes an updated taxonomy for its fundamental components (bicluster, biclustering solution, biclustering algorithms, and evaluation measures) and applications. We unify scattered concepts in the literature with new definitions to accommodate the diversity of data types (such as tabular, network, and time series data) and the specificities of biological and biomedical data domains. We further propose a pipeline for biclustering data analysis and discuss practical aspects of incorporating biclustering in real-world applications. We highlight prominent application domains, particularly in bioinformatics, and identify typical biclusters to illustrate the analysis output. Moreover, we discuss important aspects to consider when choosing, applying, and evaluating a biclustering algorithm. We also relate biclustering with other data mining tasks (clustering, pattern mining, classification, triclustering, N-way clustering, and graph mining). Thus, it provides theoretical and practical guidance on biclustering data analysis, demonstrating its potential to uncover actionable insights from complex datasets.

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Section: Evaluation Measuresmentioning

confidence: 99%

Biclustering data analysis: a comprehensive survey

Castanho,

Aidos,

Madeira

2024

Briefings in Bioinformatics

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“…MicroRNAs (miRNAs), a type of small noncoding RNAs with a length of about 19–25nt, , participate in various cellular processes such as cell formation, differentiation, proliferation, and apoptosis. , Most miRNAs interact with the 3′ untranslated region (3′ UTR) of target mRNAs to induce their degradation and translational repression. − In recent years, many studies have shown that miRNAs play a non-negligible role in the regulation of drug response among different cancers. ,, Especially, some miRNAs are potential therapeutic targets to enhance drug sensitivity. − For example, inhibition of miR-21 and miR-200b can increase sensitivity to Gemcitabine in human cholangiocarcinoma cells . Upregulation of miR-155-5p enhanced the sensitivity of liver carcinoma cells to Adriamycin and promoted apoptosis through the inhibition of autophagy in vitro .…”

Section: Introductionmentioning

confidence: 99%

Dual-Channel Heterogeneous Graph Neural Network for Predicting microRNA-Mediated Drug Sensitivity

Deng

Fan

Xiao

et al. 2022

J. Chem. Inf. Model.

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Many studies have confirmed that microRNAs (miRNAs) are mediated in the sensitivity of tumor cells to anticancer drugs. MiRNAs are emerging as a type of promising therapeutic targets to overcome drug resistance. However, there is limited attention paid to the computational prediction of the associations between miRNAs and drug sensitivity. In this work, we proposed a heterogeneous network-based representation learning method to predict miRNA-drug sensitivity associations (DGNNMDA). An miRNA-drug heterogeneous network was constructed by integrating miRNA similarity network, drug similarity network, and experimentally validated miRNA-drug sensitivity associations. Next, we developed a dual-channel heterogeneous graph neural network model to perform feature propagation among the homogeneous and heterogeneous nodes so that our method can learn expressive representations for miRNA and drug nodes. On two benchmark datasets, our method outperformed other seven competitive methods. We also verified the effectiveness of the feature propagations on homogeneous and heterogeneous nodes. Moreover, we have conducted two case studies to verify the reliability of our methods and tried to reveal the regulatory mechanism of miRNAs mediated in drug sensitivity. The source code and datasets are freely available at .

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Recent Development of Bioinformatics Tools for microRNA Target Prediction

Khatun

Alam

Shoombuatong

et al. 2022

CMC

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: MicroRNAs (miRNAs) are central players that regulate the post-transcriptional processes of gene expression. Binding of miRNAs to target mRNAs can repress their translation by inducing the degradation or by inhibiting the translation of the target mRNAs. High-throughput experimental approaches for miRNA target identification are costly and time-consuming, depending on various factors. It is vitally important to develop the bioinformatics methods for accurately predicting miRNA targets. With the increase of RNA sequences in the post-genomic era, bioinformatics methods are being developed for miRNA studies specially for miRNA target prediction. This review summarizes the current development of state-of-the-art bioinformatics tools for miRNA target prediction, points out the progress and limitations of the available miRNA databases, and their working principles. Finally, we discuss the caveat and perspectives of the next-generation algorithms for the prediction of miRNA targets.

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MMiRNA-Viewer2, a bioinformatics tool for visualizing functional annotation for MiRNA and MRNA pairs in a network

Cited by 3 publications

References 40 publications

Biclustering data analysis: a comprehensive survey

Biclustering data analysis: a comprehensive survey

Dual-Channel Heterogeneous Graph Neural Network for Predicting microRNA-Mediated Drug Sensitivity

Recent Development of Bioinformatics Tools for microRNA Target Prediction

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