Hui Yang

TL;DR: A novel predictor called iDNA6mA-PseKNC is proposed that is established by incorporating nucleotide physicochemical properties into Pseudo K-tuple Nucleotide Composition (PSEKNC), and it has been observed via rigorous cross-validations that the predictor's sensitivity, specificity, accuracy, and stability are excellent.

TL;DR: A novel platform called “iRNA-PseColl” has been developed, formed by incorporating both the individual and collective features of the sequence elements into the general pseudo K-tuple nucleotide composition (PseKNC) of RNA via the chemicophysical properties and density distribution of its constituent nucleotides.

TL;DR: The predictive results of the rigorous jackknife test and cross species test demonstrated that the performance of iDNA4mC is quite promising and holds high potential to become a useful tool for identifying 4mC sites.

TL;DR: A predictor called iRNA-AI is proposed by incorporating the chemical properties of nucleotides and their sliding occurrence density distribution along a RNA sequence into the general form of pseudo nucleotide composition (PseKNC), which has been shown by the rigorous jackknife test and independent dataset test that the performance of the proposed predictor is quite promising.

TL;DR: It has been shown via rigorous cross-validations for the RNA sequences from Homo sapiens and Mus musculus transcriptomes that the success rates achieved by the powerful new predictor are quite high and it is anticipated that iRNA-3typeA may become a useful high throughput tool for genome analysis.