ArticleBriefings in bioinformatics2025
RSCNN-PseU: random searching-based convolutional neural network model for identifying RNA pseudouridine.
Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
In order to identify RNA pseudouridine more effectively, in this paper, we propose a new feature extraction method. First, the original sequence is converted into a numerical sequence based on two physicochemical properties of dinucleotides, namely free energy and hydrophilicity; then, it is subjected to discrete Fourier transform (DFT) and the amplitude of each DFT value is calculated. In this way, for an RNA sequence of length N, we can obtain 2(N-1) features. Ultimately, we utilize a convolutional neural network for prediction, incorporating a dynamic fully connected layer within it. The random search algorithm is employed to ascertain the optimal number of fully connected layers and to fine-tune the model parameters, thereby enabling adaptive regulation of model complexity and accommodating the varying needs of different species and datasets. Experimental results have shown that our model RSCNN-PseU has better identification effect for RNA pseudouridine.
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