This is the first predictor for mouse genome. In 4mCpred-EL, we generated 28 probabilistic features based on four different machine learning algorithms (SVM, RF, ERT, & GB) and seven sequence-based feature encodings (Kmer, RFHC, BPF, LPDF, EIIP, DPCP, & TPCP) that cover diverse information, including compositional, physicochemical information, and nucleotide position-specific, etc. Consequently, these probabilistic features inputted SVM, RF, ERT & GB, whose majority vote will be considered as the final estimate. For a given DNA sequence, 4mCpred-EL predicts its calss and probability values.
| >Seq1 AATTGGATAGGGAGAAGCCGATGTAGCTGATTCTAGCAAGAGTATATAACTTTTTTCTTCAAGGCAGCAGGTGTCTGCCTAA >Seq2 GCTGCAAGCCACCAACTAGGACGTACTAGAAATCTAGAATGTAGACTATTCATTATTTATCATGCACTAATCACTTAGGTATCCGTGCGGTGCCGACACGTTAGGCGTTGGGATGGTCCATGG |
