MD-MLI: Prediction of miRNA–lncRNA Interaction by Using Multiple Features and Hierarchical Deep Learning

Song, Jing; Tian, Shengwei; Yu, Long; Yang, Qimeng; Xing, Yan; Zhang, Chao; Dai, Qigen; Duan, Xiaodong

doi:10.1109/tcbb.2020.3034922

Cited by 17 publications

(8 citation statements)

References 29 publications

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“…Network-based methods EPLMI [52], GCLMI [53], SLNPM [54], LNRLMI [56], LMNLMI [57], GEEL-PI (GEEL-FI) [58], GNMFLMI [63], LMFNRLMI [64], LMI-DForest [66], SNFHGILMI [67], LMI-INGI [68], NDALMA [69], GCNCRF [70] Sequence-based methods LncMirNet [71], CIRNN [76], PmliPred [77], PmliPEMG [79], Kang's method [81], MD-MLI [82], preMLI [84] Fig. 2: Taxonomy of computational methods for LMI prediction.…”

Section: Methodsmentioning

confidence: 99%

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A Survey of Computational Methods and Databases for lncRNA-MiRNA Interaction Prediction

Sheng

Huang

Gao

et al. 2023

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

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Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are two prevalent non-coding RNAs in current research. They play critical regulatory roles in the life processes of animals and plants. Studies have shown that lncRNAs can interact with miRNAs to participate in post-transcriptional regulatory processes, mainly involved in regulating cancer development, metastatic progression, and drug resistance. Additionally, these interactions have significant effects on plant growth, development, and responses to biotic and abiotic stresses. Deciphering the potential relationships between lncRNAs and miRNAs may provide new insights into our understanding of the biological functions of lncRNAs and miRNAs, and the pathogenesis of complex diseases. In contrast, gathering information on lncRNA-miRNA interactions (LMIs) through biological experiments is expensive and time-consuming. With the accumulation of multi-omics data, computational models are extremely attractive in systematically exploring potential LMIs. To the best of our knowledge, this is the first comprehensive review of computational methods for identifying LMIs. Specifically, we first summarized the available public databases for predicting animal and plant LMIs. Secondly, we comprehensively reviewed the computational methods for predicting LMIs and classified them into two categories, including network-based methods and sequence-based methods. Thirdly, we analyzed the standard evaluation methods and metrics used in LMI prediction. Finally, we pointed out some problems in the current study and discuss future research directions. Relevant databases and the latest advances in LMI prediction are summarized in a GitHub repository https://github.com/sheng-n/lncRNA-miRNA-interaction-methods, and we'll keep it updated.

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Section: Methodsmentioning

confidence: 99%

“…A data-driven hierarchical deep learning framework, MD-MLI, was developed by Song et al (Fig. 7) [82]. The framework can efficiently extract sequence-derived (intrinsic) and secondary structure features.…”

Section: Md-mlimentioning

confidence: 99%

A Survey of Computational Methods and Databases for lncRNA-MiRNA Interaction Prediction

Sheng

Huang

Gao

et al. 2023

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

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“…Similarly, the prediction of lncRNA–miRNA interactions has also gained substantial interest. These investigations capitalize on the ability of deep learning models to discern patterns and predict interactions [ 72 , 75 ], providing vital insights into the modulation of gene expression by lncRNAs.…”

Section: Literature Analysismentioning

confidence: 99%

Deep Learning Approaches for lncRNA-Mediated Mechanisms: A Comprehensive Review of Recent Developments

Kim

Lee

2023

IJMS

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This review paper provides an extensive analysis of the rapidly evolving convergence of deep learning and long non-coding RNAs (lncRNAs). Considering the recent advancements in deep learning and the increasing recognition of lncRNAs as crucial components in various biological processes, this review aims to offer a comprehensive examination of these intertwined research areas. The remarkable progress in deep learning necessitates thoroughly exploring its latest applications in the study of lncRNAs. Therefore, this review provides insights into the growing significance of incorporating deep learning methodologies to unravel the intricate roles of lncRNAs. By scrutinizing the most recent research spanning from 2021 to 2023, this paper provides a comprehensive understanding of how deep learning techniques are employed in investigating lncRNAs, thereby contributing valuable insights to this rapidly evolving field. The review is aimed at researchers and practitioners looking to integrate deep learning advancements into their lncRNA studies.

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“…More complex networks are currently being developed that may help not only to classify ncRNAs but also to discover their modes of action. Song et al [2020] developed a complex framework involving capsule, a recurrent and long-short-term memory network to analyze interactions between lncRNAs and miRNAs. The method has proven to be generalizable after testing with several datasets.…”

Section: Concluding Remarks and Perspectivesmentioning

confidence: 99%

Non-Coding RNAs: lncRNAs, miRNAs, and piRNAs in Sexual Development

Burgos

Hurtado

Jiménez

et al. 2021

Sex Dev

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Non-coding RNAs (ncRNAs) are a group of RNAs that do not encode functional proteins, including long non-coding RNAs (lncRNAs), microRNAs (miRNAs), PIWI-interacting RNAs (piRNAs), and short interfering RNAs (siRNAs). In the last 2 decades an effort has been made to uncover the role of ncRNAs during development and disease, and nowadays it is clear that these molecules have a regulatory function in many of the developmental and physiological processes where they have been studied. In this review, we provide an overview of the role of ncRNAs during gonad determination and development, focusing mainly on mammals, although we also provide information from other species, in particular when there is not much information on the function of particular types of ncRNAs during mammalian sexual development.

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MD-MLI: Prediction of miRNA–lncRNA Interaction by Using Multiple Features and Hierarchical Deep Learning

Cited by 17 publications

References 29 publications

A Survey of Computational Methods and Databases for lncRNA-MiRNA Interaction Prediction

A Survey of Computational Methods and Databases for lncRNA-MiRNA Interaction Prediction

Deep Learning Approaches for lncRNA-Mediated Mechanisms: A Comprehensive Review of Recent Developments

Non-Coding RNAs: lncRNAs, miRNAs, and piRNAs in Sexual Development

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