Protein sequence classification through relevant sequence mining and Bayes Classifiers

• Tipo: Artigo em Revista Científica Internacional
• Data: 2005

Autores

Ferreira, PG (Autor) Outra; Azevedo, PJ (Autor) Outra

Indexação

ISI Web of Knowledge - 12 Citações

Scopus - 16 Citações

Classificação Científica

CORDIS: Ciências Físicas > Ciência de computadores > Informática

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: P-000-6MM

DOI: 10.1007/11595014_24

Abstract (EN): We tackle the problem of sequence classification using relevant subsequences found in a dataset of protein labelled sequences. A subsequence is relevant if it is frequent and has a minimal length. For each query sequence a vector of features is obtained. The features consist in the number and average length of the relevant subsequences shared with each of the protein families. Classification is performed by combining these features in a Bayes Classifier. The combination of these characteristics results in a multi-class and multi-domain method that is exempt of data transformation and background knowledge. We illustrate the performance of our method using three collections of protein datasets. The performed tests showed that the method has an equivalent performance to state of the art methods in protein classification.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12