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Authors: Simos Kazantzidis ; Anastasia Krithara and George Paliouras

Affiliation: National Center for Scientific Research (NCSR) “Demokritos”, Greece

Keyword(s): Splice Site Recognition, Transfer Learning, Classification.

Abstract: As more genomes are sequenced, there is an increasing need for automated gene prediction. One of the subproblems of the gene prediction, is the splice sites recognition. In eukaryotic genes, splice sites mark the boundaries between exons and introns. Even though, there are organisms which are well studied and their splice sites are known, there are plenty others which have not been studied well enough. In this work, we propose two transfer learning approaches for the splice site recognition problem, which take into account the knowledge we have from the well-studied organisms. We use different representations for the sequences such as the n-gram graph representation and a representation based on biological motifs. Furthermore, we study the case where more than one organisms are available for training and we incorporate information from the phylogenetic analysis between organisms. An extensive evaluation has taken place. The results indicate that the proposed representations and appro aches are very promising. (More)

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Paper citation in several formats:
Kazantzidis, S.; Krithara, A. and Paliouras, G. (2017). Splice Site Prediction: Transferring Knowledge Across Organisms. In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - BIOINFORMATICS; ISBN 978-989-758-214-1; ISSN 2184-4305, SciTePress, pages 160-167. DOI: 10.5220/0006164401600167

@conference{bioinformatics17,
author={Simos Kazantzidis. and Anastasia Krithara. and George Paliouras.},
title={Splice Site Prediction: Transferring Knowledge Across Organisms},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - BIOINFORMATICS},
year={2017},
pages={160-167},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006164401600167},
isbn={978-989-758-214-1},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - BIOINFORMATICS
TI - Splice Site Prediction: Transferring Knowledge Across Organisms
SN - 978-989-758-214-1
IS - 2184-4305
AU - Kazantzidis, S.
AU - Krithara, A.
AU - Paliouras, G.
PY - 2017
SP - 160
EP - 167
DO - 10.5220/0006164401600167
PB - SciTePress

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