Protein sequence classification through relevant sequence mining and Bayes Classifiers

• Type: Article in International Scientific Journal
• Date: 2005

Authors

Ferreira, PG (Author) Other; Azevedo, PJ (Author) Other

Indexing

ISI Web of Knowledge - 12 Citations

Scopus - 16 Citations

Scientific classification

CORDIS: Physical sciences > Computer science > Informatics

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12