Computational Approaches for Non-Coding RNA Prediction Studies

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Background

The normal function of cells relies on the accurate expression of a large number of protein coding RNAs, messenger RNAs (mRNAs), and non-coding RNAs (ncRNAs). Depending on the function and length of ncRNAs they may also be classed as intronic RNA, microRNA (miRNA), long noncoding RNA (lncRNA), circular RNA (circRNA), etc. It is well established that ncRNAs may affect normal gene expression and disease progression, and as such have become a new class of targets for drug discovery. Moreover, different classes of ncRNAs show various activities that are of great importance for cellular function and development. Therefore, more research is needed to study the regulatory interactions between ncRNAs classes, as well as the associations between ncRNA and other biological entities including disease, small molecules, protein, etc. Predicting ncRNA related network will greatly expand our understanding of ncRNA functions and their coordinated regulatory networks.

Predicting ncRNA-related biological entities based on biological experiments is usually time-consuming and labor-intensive. With the increase of multi-source biological data in public databases, it is good choice to design computational models to predict ncRNA related networks, such as disease-miRNA associations, circRNA-lncRNA interactions, small molecular-lncRNA associations and any other biological network where the associations between nodes in the network are to be analyzed and predicted. Simultaneously, machine learning-based and complex network-based methods have not only become more powerful, but also more robust. These advances have accelerated the application of machine learning-based and complex network-based method in predicting ncRNA related networks.

The aim of this Research Topic is to collect articles associated with computational approaches for the prediction of ncRNA-related networks. We encourage manuscripts to cover various computational methods and tools that use machine learning-based or complex network-based approaches to identify ncRNA-related networks. We welcome submissions covering, but not limited to, the following:

• Wet-lab experimental validation and clinical applications of ncRNA-related biological entities
• Computational models for prediction of interactions between ncRNAs classes, for example, miRNA-lncRNA interactions, circRNA-miRNA interactions, miRNA-competing endogenous RNA interactions, etc.
• Computational models for prediction of associations between ncRNA and other biological entities, for example, circRNA-protein interactions, lncRNA-protein interactions, lncRNA-disease associations, miRNA-disease associations, circRNA-disease associations, miRNA-small molecular associations, lncRNA-small molecular associations, circRNA-small molecular associations, intronic RNA-small molecule associations, etc.
• Computational models for prediction of ncRNA biomarkers for different diseases
• Computational models for prediction of ncRNA binding sites
• Computational models for Prediction of ncRNA function
• Computational models for prediction and analysis of ncRNA related regulatory network in different diseases

Keywords: machine learning, complex network, non-coding RNA, prediction, computational model

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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