Application of Deep Learning Models to MicroRNA Transcription Start Site Identification

Barham, Clayton; Cha, Mingyu; Li, Xiaoman; Hu, Haiyan

doi:10.1109/icbcb.2019.8854645

Cited by 7 publications

(4 citation statements)

References 24 publications

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“…More than a dozen studies previously predicted or annotated miRNA TSSs [20,29,32,[51][52][53]. These predicted or annotated TSSs were often inconsistent between different studies [29,32].…”

Section: Discussionmentioning

confidence: 99%

A computational modeling of pri-miRNA expression

Zheng,

Wang,

et al. 2024

PLoS ONE

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MicroRNAs (miRNAs) play crucial roles in gene regulation. Most studies focus on mature miRNAs, which leaves many unknowns about primary miRNAs (pri-miRNAs). To fill the gap, we attempted to model the expression of pri-miRNAs in 1829 primary cell types, cell lines, and tissues in this study. We demonstrated that the expression of pri-miRNAs can be modeled well by the expression of specific sets of mRNAs, which we termed their associated mRNAs. These associated mRNAs differ from their corresponding target mRNAs and are enriched with specific functions. Most associated mRNAs of a miRNA are shared across conditions, while on average, about one-fifth of the associated mRNAs are condition-specific. Our study shed new light on understanding miRNA biogenesis and general gene transcriptional regulation.

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“…More than a dozen studies previously predicted or annotated miRNA TSSs [20,29,32,[51][52][53]. These predicted or annotated TSSs were often inconsistent between different studies [29,32].…”

Section: Discussionmentioning

confidence: 99%

A computational modeling of pri-miRNA expression

Zheng,

Wang,

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…More than a dozen studies previously predicted or annotated miRNA TSSs [19,22,[34][35][36][37]. These predicted or annotated TSSs were often inconsistent between different studies [19,22].…”

Section: Discussionmentioning

confidence: 99%

A computational modeling of primary-microRNA expression

Zheng

Wang

et al. 2022

Preprint

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MicroRNAs (miRNAs) play crucial roles in gene regulation. Most studies so far focus on mature miRNAs, which leaves many gaps in our knowledge in primary miRNAs (pri-miRNA). To fill these gaps, we attempted to model the expression of pri-miRNAs in 1829 primary cell types and tissues in this study. We demonstrated that the expression of their associated mRNAs could model the expression of the pri-miRNAs well. These associated mRNAs are different from their corresponding target mRNAs and are enriched with specific functions. The majority of the associated mRNAs of a miRNA are shared across conditions, although a fraction of the associated mRNAs are condition-specific. Our study shed new light on the understanding of miRNA biogenesis and general gene transcriptional regulation.

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“…Deep Learning models are infamous for being a black box when it comes to understanding the underlying features. But recent studies have focused on various strategies that can reveal the features or patterns learned by different types of machine learning models [38][39][40][41][42]. Here, two of the most popular feature identification methods, convolutional kernel analysis and input perturbation, were applied to discover important features for miRNA/isomiR-mRNA interactions [26].…”

Section: Feature Identificationmentioning

confidence: 99%

A Deep Learning Method for MiRNA/IsomiR Target Detection

Talukder

Zhang

et al. 2022

Preprint

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MotivationAccurate identification of microRNA (miRNA) targets at base-pair resolution has been an open problem for over a decade. The recent discovery of miRNA isoforms (isomiRs) adds more complexity to this problem. Despite the existence of many methods, none considers isomiRs, and their performance is still suboptimal. We hypothesize that by taking the isomiR-mRNA interaction into account and applying a deep learning model to study miRNA-mRNA interaction features, we may improve the accuracy of miRNA target predictions.ResultsWe developed a deep learning tool called DMISO to capture the intricate features of miRNA/isomiR-mRNA interactions. Based on 10-fold cross-validation, DMISO showed high precision (95%) and recall (90%). Evaluated on three independent datasets, DMISO had superior performance to five tools, including three popular conventional tools and two recently developed deep learning-based tools. By applying two popular feature interpretation strategies, we demonstrated the importance of the miRNA regions other than their seeds and the potential contribution of the RNA-binding motifs within miRNAs/isomiRs and mRNAs to the miRNA/isomiR-mRNA interactions.AvailabilityThe source code and tool are available at http://hulab.ucf.edu/research/projects/DMISO.Contacthaihu@cs.ucf.edu, xiaoman@mail.ucf.eduSupplementary informationSupplementary data are available online.

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Application of Deep Learning Models to MicroRNA Transcription Start Site Identification

Cited by 7 publications

References 24 publications

A computational modeling of pri-miRNA expression

A computational modeling of pri-miRNA expression

A computational modeling of primary-microRNA expression

A Deep Learning Method for MiRNA/IsomiR Target Detection

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