  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.
