Feature extraction in protein sequences classification : a new stability measure
This work addresses the need for robust feature extraction in bioinformatics, but it is incremental as it evaluates existing methods rather than proposing a new one.
The paper tackled the problem of evaluating the robustness of feature extraction methods for protein sequence classification by introducing a stability measure for motifs, and found that existing methods vary in their ability to detect changes and target interesting motifs.
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.