This is the first meta predictor for 4mC site prediction. In meta-4mCpred, we employed a feature representation learning scheme and generated 56 features based on four different ML (machine learning) algorithms and seven sequence-based feature encodings that cover diverse information, including compositional, physicochemical information, and nucleotide position-specific, etc. Consequently, these features were used as an input to support vector machine to build the meta-prediction models. For a given DNA sequence, Meta-4mCpred predicts its calss and probability values.
>P1
GCACCCCGCCAGAAAGACCACCGGAAATTCGAGCCCCTTGG
>P2
CTTCCACGGCGGAAAGATAGCGGGAGAAATCGACCCTATGC
>P3
TCTTCCATCTGGCGTATTTTCAGTTGAGTGAAATCGGGCTC
>N1
CCGCCGACGAGGTGGGGGAACTGGCCGAGGCATTCAATCGC
>N2
GTTAGAGAAGACCGCTCCCTCCATGAACTGCGTCACGTTGC
>N3
GCCTTTGCCGGCGCCGTGGCCATCTTCAACCGGAAAATCGA
