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Table of Content

Computational Models in Non-Coding RNA and Human Disease

Submission Deadline: 31 December 2021 (closed)

Guest Editors

Prof. Dr. Qi Zhao, University of Science and Technology Liaoning, China. zhaoqi@lnu.edu.cn

Prof. Dr. Liang Wang, Xuzhou Medical University, China. leonwang@xzhmu.edu.cn


Non-coding RNA (ncRNA) is an RNA molecule that does not encode a protein, but this does not mean that such RNAs do not contain information nor have function. Although it has been generally assumed that most genetic information is transacted by proteins, recent evidence suggests that the majority of the genomes of mammals and other complex organisms is in fact transcribed into ncRNAs, many of which are alternatively spliced and/or processed into smaller products. Abundant and functionally important types of ncRNAs include transfer RNAs (tRNAs) and ribosomal RNAs (rRNAs), as well as small RNAs such as microRNAs (miRNAs), siRNAs, piRNAs, snoRNAs, snRNAs, exRNAs, scaRNAs and the long ncRNAs (lncRNAs) such as Xist and HOTAIR. As is known, RNA regulatory networks determine most of our complex characteristics and play a significant role in disease and constitute an unexplored world of genetic variation both within and between species. Recent transcriptomic and bioinformatic studies suggest that the ncRNAs appear to comprise a hidden layer of internal signals that control various levels of gene expression in physiology and development, including chromatin architecture/epigenetic memory, transcription, RNA splicing, editing, translation and turnover. Furthermore, ncRNAs have been also revealed to contribute to diseases including cancer, autism, Alzheimer's and so on. Predicting ncRNA–disease associations could not only boost human disease diagnostic and prognostic, but also improve the new drug development.


However, little efforts have been attempted to understand and predict ncRNA–disease associations on a large scale until now. And the traditional methods are both expansive and time-consuming. In contrast to the traditional experimental approach, the aim of us is to assess these ncRNA-disease associations on a large-scale in human with the computational methods based on the big data accumulated by the previous experimental methods. To find associations between the ncRNAs and their corresponding diseases, various statistical and computational techniques could be employed. Meanwhile, benefitting from the rapid development of artificial intelligence, accumulating computational methods of analyses and prediction on large scale data have been developed to work for various fields related to data science. As the previous traditional experimental results revealed, the ncRNA-disease pairs appeared some laws to be associated with each other.


Therefore, it is feasible and necessary to build advanced intelligent algorithm or computational models to reveal the ncRNA-disease associations. Furthermore, with the help of the computational methods, the experimental analyses of associations between ncRNA and disease or other instances could be more convenient and accurate. We can look forward that more and more computational models on ncRNA-disease association prediction will be developed to promote the further experimental studies with our best efforts. Similar to the researches on ncRNA-disease association, the researches on ncRNA-protein interaction, function and structure of ncRNAs, and even drug effect associated with ncRNAs are all the fields that we will devote ourselves to. We hence invite investigators to contribute research article, reviews, and commentaries describing recent findings which use computational techniques for the research of ncRNAs.


ncRNA-Disease Association Prediction, ncRNA-Protein Interaction Prediction, ncRNA Function Prediction, ncRNA Structure Prediction, ncRNA and Drug Effect

Published Papers

  • Open Access


    MDA-TOEPGA: A novel method to identify miRNA-disease association based on two-objective evolutionary programming genetic algorithm

    BIOCELL, Vol.46, No.8, pp. 1925-1933, 2022, DOI:10.32604/biocell.2022.019613
    (This article belongs to the Special Issue: Computational Models in Non-Coding RNA and Human Disease)
    Abstract The association between miRNA and disease has attracted more and more attention. Until now, existing methods for identifying miRNA related disease mainly rely on top-ranked association model, which may not provide a full landscape of association between miRNA and disease. Hence there is strong need of new computational method to identify the associations from miRNA group view. In this paper, we proposed a framework, MDA-TOEPGA, to identify miRNAdisease association based on two-objective evolutionary programming genetic algorithm, which identifies latent miRNAdisease associations from the view of functional module. To understand the miRNA functional module in diseases, the case study is presented.… More >

  • Open Access


    INTS-MFS: A novel method to predict microRNA-disease associations by integrating network topology similarity and microRNA function similarity

    BIOCELL, Vol.46, No.3, pp. 837-845, 2022, DOI:10.32604/biocell.2022.017538
    (This article belongs to the Special Issue: Computational Models in Non-Coding RNA and Human Disease)
    Abstract Identifying associations between microRNAs (miRNAs) and diseases is very important to understand the occurrence and development of human diseases. However, these existing methods suffer from the following limitation: first, some disease-related miRNAs are obtained from the miRNA functional similarity networks consisting of heterogeneous data sources, i.e., disease similarity, protein interaction network, gene expression. Second, little approaches infer disease-related miRNAs depending on the network topological features without the functional similarity of miRNAs. In this paper, we develop a novel model of Integrating Network Topology Similarity and MicroRNA Function Similarity (INTS-MFS). The integrated miRNA similarities are calculated based on miRNA functional similarity… More >

  • Open Access


    Potential genomic biomarkers of obesity and its comorbidities for phthalates and bisphenol A mixture: In silico toxicogenomic approach

    BIOCELL, Vol.46, No.2, pp. 519-533, 2022, DOI:10.32604/biocell.2022.018271
    (This article belongs to the Special Issue: Computational Models in Non-Coding RNA and Human Disease)
    Abstract This in silico toxicogenomic study aims to explore the relationship between phthalates and bisphenol A (BPA) co-exposure and obesity, as well as its comorbid conditions, in order to construct a possible set of genomic biomarkers. The Comparative Toxicogenomics Database (CTD; http://ctd.mdibl.org) was used as the main data mining tool, along with GeneMania (https://genemania.org), ToppGene Suite (https://toppgene.cchmc.org) and DisGeNET (http://www.disgenet.org). Among the phthalates, bis(2-ethylhexyl) phthalate (DEHP) and dibutyl phthalate (DBP) were chosen as the most frequently curated phthalates in CTD, which also share similar mechanisms of toxicity. DEHP, DBP and BPA interacted with 84, 90 and 194 obesity-related genes/proteins, involved in… More >

  • Open Access


    Construction and validation of prognostic model based on autophagy-related lncRNAs in gastric cancer

    BIOCELL, Vol.46, No.1, pp. 97-109, 2022, DOI:10.32604/biocell.2021.015608
    (This article belongs to the Special Issue: Computational Models in Non-Coding RNA and Human Disease)
    Abstract Gastric cancer (GC) is one of the most common cancer worldwide. Although emerging evidence indicates that autophagy-related long non-coding RNA (lncRNA) plays an important role in the progression of GC, the prognosis of GC based on autophagy is still deficient. The Cancer Genome of Atlas stomach adenocarcinoma (TCGA-STAD) dataset was downloaded and separated into a training set and a testing set randomly. Then, 24 autophagy-related lncRNAs were found strongly associated with the survival of the TCGA-STAD dataset. 11 lncRNAs were selected to build the risk score model through the least absolute shrinkage and selection operator (LASSO) regression. Every patient got… More >

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