ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2008
Variable-length positional modeling for biological sequence classification.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Selecting the most informative features in supervised biological classification problems is a decisive pre-processing step for two main reasons: (1) to deal with the dimensionality reduction problem, and (2) to ascribe biological meaning to the underlying feature interactions. This paper presents a filter-based feature selection method that is suitable for positional modeling of biological sequences. The basic motivation is the problem of using a positional model of fixed length that sub-optimally describes biological sequences in a specific classification problem. The core filtering criterion is the F-score and the source features are the positional probabilities describing variable-length interactions among residues. The proposed method was evaluated on human splice sites classification using a linear SVM classifier. The method yields to superior classification accuracy compared to the individual positional models, while it maintains the space complexity of the individual models, in a time-efficient way and independently of the classifier.
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18999162PMC2656059What OpenQuestion holds
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