AtLASS: A Scheme for End-to-End Prediction of Splice Sites Using Attention-based Bi-LSTM
Author:
Affiliation:
1. Graduate School of Science and Technology, University of Tsukuba
2. Department of Clinical Medicine, University of Tsukuba
3. Center for Computational Sciences, University of Tsukuba
Publisher
Information Processing Society of Japan
Subject
Computer Science Applications,Biochemistry, Genetics and Molecular Biology (miscellaneous)
Link
https://www.jstage.jst.go.jp/article/ipsjtbio/16/0/16_20/_pdf
Reference48 articles.
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3. [3] Hoff, K.J. and Stanke, M.: Current methods for automated annotation of protein-coding genes, Current Opinion in Insect Science, Vol.7, pp.8-14 (2015).
4. [4] Ter-Hovhannisyan, V., Lomsadze, A., Chernoff, Y.O. and Borodovsky, M.: Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training, Genome Research, Vol.18, No.12, pp.1979-1990 (2008).
5. [5] Lomsadze, A., Burns, P.D. and Borodovsky, M.: Integration of mapped RNA-Seq reads into automatic training of eukaryotic gene finding algorithm, Nucleic Acids Research, Vol.42, No.15, pp.e119-e119 (2014).
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1. Splice site recognition - deciphering Exon-Intron transitions for genetic insights using Enhanced integrated Block-Level gated LSTM model;Gene;2024-07
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