Comput Biol Chem - Classification of splice-junction sequences via weighted position specific scoring approach.

Tópicos

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{ first(2504) two(1366) second(1323) }
{ gene(2352) biolog(1181) express(1162) }
{ sequenc(1873) structur(1644) protein(1328) }
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Resumo

The prediction of the complete structure of genes is one of the very important tasks of bioinformatics, especially in eukaryotes. A crucial part in the gene structure prediction is to determine the splice sites in the coding region. Identification of splice sites depends on the precise recognition of the boundaries between exons and introns of a given DNA sequence. This problem can be formulated as a classification of sequence elements into 'exon-intron' (EI), 'intron-exon' (IE) or 'None' (N) boundary classes. In this study we propose a new Weighted Position Specific Scoring Method (WPSSM) to recognize splice sites which uses a position-specific scoring matrix constructed by nucleotide base frequencies. A genetic algorithm is used in order to tune the weight and threshold parameters of the positions on. This method consists of two phases: learning phase and identification phase. The proposed WPSS method poses efficient results compared with the performance of many methods proposed in the literature. Computational experiments are performed on the DNA sequence datasets from 'UCI Repository of machine learning databases'.

Resumo Limpo

predict complet structur gene one import task bioinformat especi eukaryot crucial part gene structur predict determin splice site code region identif splice site depend precis recognit boundari exon intron given dna sequenc problem can formul classif sequenc element exonintron ei intronexon ie none n boundari class studi propos new weight posit specif score method wpssm recogn splice site use positionspecif score matrix construct nucleotid base frequenc genet algorithm use order tune weight threshold paramet posit method consist two phase learn phase identif phase propos wpss method pose effici result compar perform mani method propos literatur comput experi perform dna sequenc dataset uci repositori machin learn databas

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