Abstract
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.
Keywords
- Frequent Pattern
- Query Sequence
- Pattern Mining
- Precision Rate
- Sequence Pattern Mining
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Ferreira, P.G., Azevedo, P.J. (2005). Protein Sequence Classification Through Relevant Sequence Mining and Bayes Classifiers. In: Bento, C., Cardoso, A., Dias, G. (eds) Progress in Artificial Intelligence. EPIA 2005. Lecture Notes in Computer Science(), vol 3808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11595014_24
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DOI: https://doi.org/10.1007/11595014_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30737-2
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