Abstract
The rapid growth of protein sequence databases is exceeding the capacity of biochemically and structurally characterizing new proteins. Therefore, it is very important the development of tools to locate, within protein sequences, those subsequences with an associated function or specific feature. In our work, we propose a method to predict one of those functional motifs (coiled coil), related with protein interaction. Our approach uses even linear languages inference to obtain a transductor which will be used to label unknown sequences. The experiments carried out show that our method outperforms the results of previous approaches.
Work supported by the CICYT TIC2000-1153 and the Generalitat Valenciana GV06/068.
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Peris, P., López, D., Campos, M., Sempere, J.M. (2006). Protein Motif Prediction by Grammatical Inference. In: Sakakibara, Y., Kobayashi, S., Sato, K., Nishino, T., Tomita, E. (eds) Grammatical Inference: Algorithms and Applications. ICGI 2006. Lecture Notes in Computer Science(), vol 4201. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11872436_15
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