Evaluation of noise reduction techniques in the splice junction recognition problem

Genetics and Molecular Biology

Ana C. Lorena¹  André C. P. L. F. De Carvalho¹ 

[1] ,Universidade de São Paulo Instituto de Ciências Matemáticas e de Computação Laboratório de Computação BioinspiradaSão Carlos São Paulo ,Brazil

关键词: pre-processing;    machine learning;    splice junction recognition;   

DOI  :  10.1590/S1415-47572004000400031

来源: SciELO

【 摘 要 】

The Human Genome Project has generated a large amount of sequence data. A number of works are currently concerned with analyzing these data. One of the analyses carried out is the identification of genes' structures on the sequences obtained. As such, one can search for particular signals associated with gene expression. Splice junctions represent a type of signal present on eukaryote genes. Many studies have applied Machine Learning techniques in the recognition of such regions. However, most of the genetic databases are characterized by the presence of noisy data, which can affect the performance of the learning techniques. This paper evaluates the effectiveness of five data pre-processing algorithms in the elimination of noisy instances from two splice junction recognition datasets. After the pre-processing phase, two learning techniques, Decision Trees and Support Vector Machines, are employed in the recognition process.

【 授权许可】

CC BY   
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