Click to view the descriptions of different models.
Click to view the descriptions of different models.
Click to view the descriptions of different models.
A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models
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identification of RNA 5-methyluridine modification sites based on multi-view features of physicochemical features and distributed representation
Read MorePrediction of RNA 5-methyluridine sites using multi-scale convolutional neural network with BiLSTM
Read MoreA sequence based predictor for identifying 5-methyluridine modification sites in Saccharomyces cerevisiae
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A transformer architecture based on multi-sense-scaled embedding features and convolutional neural network to identify RNA N7-methylguanosine sites
Read MoreIdentifying N7-methylguanosine sites based on dinucleotide physicochemical properties of RNA
Read MoreAn improved machine learning-based model for predicting internal m7G modifications using sequence properties
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