Abstract
Prediction of Alternative Splicing has been traditionally based on expressed sequences’ study, helped by homology considerations and the analysis of local discriminative features. More recently, Machine Learning algorithms have been developed that try avoid the use of a priori information, with partial success. Here we approach the prediction of Alternative Splicing as a time series analysis problem and we show that it is possible to obtain results similar or better than the state of the art without any explicit modeling of homology, positions in the splice site, nor any use of other local features. As a consequence, our method has a better generality and a broader and simpler applicability with respect to previous ones. Results on pre-mRNA sequences in C.Elegans are reported.
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Ceccarelli, M., Maratea, A. (2007). An Alternative Splicing Predictor in C.Elegans Based on Time Series Analysis. In: Masulli, F., Mitra, S., Pasi, G. (eds) Applications of Fuzzy Sets Theory. WILF 2007. Lecture Notes in Computer Science(), vol 4578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73400-0_75
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DOI: https://doi.org/10.1007/978-3-540-73400-0_75
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