Name
Abstract | ||
|---|---|---|
Recently next generation sequencing techniques have begun to produce huge amounts of sequencing data. To analyze these data, an efficient method that can handle large amounts of information is required. In this paper, we proposed a method for classifying sets of DNA sequences by using a hidden Markov model self-organizing map. For this purpose, a learning algorithm that requires low computational costs was developed. The availability of this method was examined in experiments classifying DNA sequences of various types of genes. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CIBCB.2013.6595411 | Computational Intelligence in Bioinformatics and Computational Biology |
Keywords | Field | DocType |
DNA,biology computing,genetics,hidden Markov models,learning (artificial intelligence),pattern classification,self-organising feature maps,sequences,DNA sequences classification,DNA sequences mapping,genes,hidden Markov model,learning algorithm,next generation sequencing techniques,self-organizing map,sequencing data | Computer science,Self-organizing map,Artificial intelligence,DNA sequencing,Bioinformatics,Hidden Markov model,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiroshi Dozono | 1 | 18 | 8.91 |
| Niina, G. | 2 | 0 | 0.34 |