Name
Abstract | ||
|---|---|---|
Sequence alignment is a very important problem in Bioinformatics since it is very useful to discover relationships among biological sequences. In this article we propose a new sequence alignment algorithm that is able to identify sequence similarities in linear space. Our solution is capable of using several parameters specifying in which conditions a given residue can be aligned. This decision is taken based on a perceptron neuron and the best set of parameters is found using simulated annealing. Comparing our algorithm to a well established algorithm, we found that for pairwise alignments, our approach was faster for all instances tested with speedups up to 7.63 and yields better quality results for the majority of the instances. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/BIBMW.2011.6112353 | BIBM Workshops |
Keywords | Field | DocType |
simulated annealing,perceptron neuron,important problem,best set,pairwise alignment,sequence alignment,biological sequence,new sequence alignment algorithm,sequence similarity,linear space,bioinformatics,genomics,perceptrons | Simulated annealing,Sequence alignment,Pairwise comparison,Alignment-free sequence analysis,Computer science,Linear space,Artificial intelligence,Bioinformatics,Multiple sequence alignment,Perceptron,Machine learning | Conference |
ISSN | Citations | PageRank |
2163-6966 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jan M. Correa | 1 | 0 | 0.68 |
| Alba Cristina Magalhaes Alves De Melo | 2 | 253 | 33.90 |
| Ricardo P. Jacobi | 3 | 90 | 16.50 |
| Azzedine Boukerche | 4 | 4301 | 418.60 |