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Feature extraction in protein sequences classification: a new stability measure

Abstract : Feature extraction is an unavoidable task, especially in the critical step of preprocessing biological sequences. This step consists for example in transforming the biological sequences into vectors of motifs where each motif is a subsequence that can be seen as a property (or attribute) characterizing the sequence. Hence, we obtain an object-property table where objects are sequences and properties are motifs extracted from sequences. This output can be used to apply standard machine learning tools to perform data mining tasks such as classification. Several previous works have described feature extraction methods for bio-sequence classification, but none of them discussed the robustness of these methods when perturbing the input data. In this work, we introduce the notion of stability of the generated motifs in order to study the robustness of motif extraction methods. We express this robustness in terms of the ability of the method to reveal any change occurring in the input data and also its ability to target the interesting motifs. We use these criteria to evaluate and experimentally compare four existing extraction methods for biological sequences.
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Contributor : Sabeur Aridhi <>
Submitted on : Wednesday, June 20, 2018 - 3:41:37 PM
Last modification on : Wednesday, February 24, 2021 - 4:24:02 PM



Saidi Rabie, Sabeur Aridhi, Engelbert Mephu Nguifo, Mondher Maddouri. Feature extraction in protein sequences classification: a new stability measure. ACM BCB 2012 - Conference on Bioinformatics, Computational Biology and Biomedicine, Oct 2012, Orlando, United States. ⟨10.1145/2382936.2383060⟩. ⟨hal-01819547⟩



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