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Splice Form Prediction using Machine Learning

MPS-Authors
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Raetsch,  G
Friedrich Miescher Laboratory, Max Planck Society;

Sommer ,  R
Max Planck Institute for Developmental Biology, Max Planck Society;

/persons/resource/persons84193

Schölkopf,  B
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Raetsch, G., Sonnenburg, S., Srinivasan, J., Müller, K.-R., Sommer, R., & Schölkopf, B. (2006). Splice Form Prediction using Machine Learning. Poster presented at 14th International Conference on Intelligent Systems for Molecular Biology (ISMB 2006), Fortaleza, Brazil.


Cite as: http://hdl.handle.net/21.11116/0000-0004-B622-8
Abstract
Accurate ab initio gene finding is still a major challenge in computational biology. We employ cutting edge machine learning similar to Hidden-Markov-SVMs to assay and improve the accuracy of genome annotations. We applied our system on the C_elegans genome and were able to drastically improve its annotation.