Welcome to the Home Page of SDM6A

SMD6A is a two-layer ensemble method. This method utilizes five different feature encodings (RFHC, KNN, NUM, MBE, and DPE_LPF) and two classifiers (SVM and ERT). In the first-layer, five different encodings are integrated through ensemble approach indvidually for SVM and ERT. Whereas the second-layer, integrates SVM and ERT to make a final prediction. For a given DNA sequence, SMD6A predicts its calss and probability values.

Paste or upload Rice DNA sequence below (The maximum sequence length should be 100000 bp)




File:



Sample FASTA sequences

  >Seq1
AATTGGATAGGGAGAAGCCGATGTAGCTGATTCTAGCAAGAGTATATAACTTTTTTCTTCAAGGCAGCAGGTGTCTGCCTAA
>Seq2
GCTGCAAGCCACCAACTAGGACGTACTAGAAATCTAGAATGTAGACTATTCATTATTTATCATGCACTAATCACTTAGGTATCCGTGCGGTGCCGACACGTTAGGCGTTGGGATGGTCCATGG		  

 


Reference

SDM6A A web-based integrative machine-learning framework for predicting 6mA sites in the rice genome (Submitted).  
[Please cite this paper if you find SDM6A useful in your research] 

Contact: bala@kias.re.kr