Protein sequence pattern mining with constraints

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

Autores

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

Indexação

ISI Web of Knowledge - 18 Citações

Scopus - 31 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-000-65V

DOI: 10.1007/11564126_14

Abstract (EN): Considering the characteristics of biological sequence databases, which typically have a small alphabet, a very long length and a relative small size (several hundreds of sequences), we propose a new sequence mining algorithm (gIL). gIL was developed for linear sequence pattern mining and results from the combination of some of the most efficient techniques used in sequence and itemset mining. The algorithm exhibits a high adaptability, yielding a smooth and direct introduction of various types of features into the mining process, namely the extraction of rigid and arbitrary gap patterns. Both breadth or a depth first traversal are possible. The experimental evaluation, in synthetic and real life protein databases, has shown that our algorithm has superior performance to state-of-the art algorithms. The use of constraints has also proved to be a very useful tool to specify user interesting patterns.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12