Abstract
In this work we tackle the task of detecting biological motifs, i.e. subsequences with an associated function. This task is important in bioinformatics because it is related to the prediction of the behaviour of the whole protein. Artificial neural networks are used to, somewhat, translate the sequence of amino acids of the protein into a code that shows the subsequences where the presence of the studied motif is expected. The experimentation performed prove the good performance of our approach.
Keywords
- Hide Layer
- Input Pattern
- Neural Network Approach
- Coiled Coil
- False Positive Detection
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.
Work supported by the Spanish CICYT under contract TIC2003-09319-C03-02.
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Campos, M., López, D. (2005). Neural Network Approach to Locate Motifs in Biosequences. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_23
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DOI: https://doi.org/10.1007/11578079_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
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