Noncoding RNAs (ncRNAs) are an important part of genes and having an important role in human cellular activities and serious diseases. To predict ncRNAs structure, there are many computational intelligence algorithms (CIAs) that are developed in past studies. However, many studies suggested that there were still many structures that are still unpredictable by researchers. In this paper, CIAs algorithms were comprehensively reviewed to predict ncRNAs structures. The advantages and disadvantages of CIA algorithms are briefly mentioned related to ncRNA genes. Moreover, the latest software tools are also compared and reviewed to identify the structure of ncRNAs for mining deep sequencing data. In this study, conventional machine learning algorithms are mainly focused and future trends are also described to predict ncRNAs structure. This paper concludes that there is a need for improving CIA algorithms by using deep learning architectures in terms of layers and computational complexity to predict ncRNAs structures.
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