Journal
RNA BIOLOGY
Volume 17, Issue 6, Pages 892-902Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/15476286.2020.1734382
Keywords
piRNAs; target mRNA deadenylation; feature selection; feature extraction strategies; a two-layered integrated classifier algorithm
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Funding
- Natural Science Foundation of Fujian Province [2017J01099]
- national key R&D program of China [2017YFE0130600]
- Project of marine economic innovation and development in Xiamen [16PFW034SF02]
- President Fund of Xiamen University [20720170054]
- Natural Science Foundation of China [61772441, 61472335, 61922020, 61425002, 61872007]
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Piwi-interacting RNAs (piRNAs) are indispensable in the transposon silencing, including in germ cell formation, germline stem cell maintenance, spermatogenesis, and oogenesis. piRNA pathways are amongst the major genome defence mechanisms, which maintain genome integrity. They also have important functions in tumorigenesis, as indicated by aberrantly expressed piRNAs being recently shown to play roles in the process of cancer development. A number of computational methods for this have recently been proposed, but they still have not yielded satisfactory predictive performance. Moreover, only one computational method that identifies whether piRNAs function in inducting target mRNA deadenylation been reported in the literature. In this study, we developed a two-layered integrated classifier algorithm, 2lpiRNApred. It identifies piRNAs in the first layer and determines whether they function in inducting target mRNA deadenylation in the second layer. A new feature selection algorithm, which was based on Luca fuzzy entropy and Gaussian membership function (LFE-GM), was proposed to reduce the dimensionality of the features. Five feature extraction strategies, namely, Kmer, General parallel correlation pseudo-dinucleotide composition, General series correlation pseudo-dinucleotide composition, Normalized Moreau-Broto autocorrelation, and Geary autocorrelation, and two types of classifier, Sparse Representation Classifier (SRC) and support vector machine with Mahalanobis distance-based radial basis function (SVMMDRBF), were used to construct a two-layered integrated classifier algorithm, 2lpiRNApred. The results indicate that 2lpiRNApred performs significantly better than six other existing prediction tools.
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