Welcome to the Home Page of Meta-4mCpred

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.

Please select the approapriate species for the prediction
Caenorhabditis elegans

Drosophila melanogaster

Arabidopsis thaliana

Escherichia coli

Geoalkalibacter subterraneus

Geobacter pickeringii


Meta-4mCpred: A sequence-based meta-predictor for accurate DNA N4-methylcytosine site prediction using effective feature representation (Molecular Therapy-Nucleic Acids).  
[Please cite this paper if you find Meta-4mCpred useful in your research

Contact: bala@kias.re.kr