Paper Info
Title
An Architecture Proposal Based in Intelligent Algorithms for Motifs Discovery in Genetic Expressions.
Abstract
Motifs are not random entities found in DNA chains. A motif can also be defined as not a single phenomenon. Already motifs, besides having recurring patterns in the analyzed sequence, also have a biological function. Intelligent algorithms are search techniques widely used to find approximate solutions to optimization and search patterns in the science area of computing. Finding motifs in gene sequences is one of the most important problems in bioinformatics and belongs to the class NP-Complete. Therefore, it is plausible to investigate the hybridization of consolidated tools, but limited in their performance, in combination with intelligent systems techniques. This work has the premise to show a research of the main techniques and concepts of intelligent algorithms used in discovery of patterns (motifs) in gene expression and also an in-depth study of the major bioinformatics algorithms that are used for this function in recent years by researchers. It is understood that such techniques in combination, can achieve interesting results for research in bioinformatics. Thus proposing an optimized architecture for motifs discovery in genetic expressions of promoter regions of a bacteria. Using as many intelligent algorithms such as bioinformatics algorithms and refining techniques of its main data provided by the algorithms used. Thus forming an architecture with better performance due to hybridization of consolidated tools to search for patterns in genetic expressions.
Year
DOI
Venue
2015
10.1007/978-3-319-27101-9_19
ADVANCES IN ARTIFICIAL INTELLIGENCE AND ITS APPLICATIONS, MICAI 2015, PT II
Keywords
Field
DocType
Bioinformatics,Genetic algorithm,Neural networks,Motif discovery
Architecture,Expression (mathematics),Intelligent decision support system,Computer science,Intelligent algorithms,Theoretical computer science,Motif (music),Artificial intelligence,Artificial neural network,Machine learning,Genetic algorithm
Conference
Volume
ISSN
Citations 
9414
0302-9743
0
PageRank 
References 
Authors
0.34
2
5
Name
Order
Citations
PageRank
Augusto G. Schmidt100.34
Frederico S. Kremer200.34
Vinícius Steffens Pazzini321.11
Michel S. Pedroso400.34
Marilton S. de Aguiar5176.21