SpliceRover: interpretable convolutional neural networks for improved splice site prediction

Zuallaert Jasper; Godin Frederic; Kim Mijung; Soete Arne; Saeys Yvan; De Neve Wesley

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SpliceRover: interpretable convolutional neural networks for improved splice site prediction

机译：拼接器：可解释的卷积神经网络，用于改进拼接网站预测

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Motivation: During the last decade, improvements in high-throughput sequencing have generated a wealth of genomic data. Functionally interpreting these sequences and finding the biological signals that are hallmarks of gene function and regulation is currently mostly done using automated genome annotation platforms, which mainly rely on integrated machine learning frameworks to identify different functional sites of interest, including splice sites. Splicing is an essential step in the gene regulation process, and the correct identification of splice sites is a major cornerstone in a genome annotation system.

机译：动机：在过去十年中，高通量测序的改进产生了大量基因组数据。在功能上解释这些序列并找到基因功能和调节标志的生物信号，目前主要使用自动化的基因组注释平台来完成，这些注释平台主要依赖于集成机器学习框架来识别不同的兴趣功能，包括拼接位点。拼接是基因调节过程中的重要步骤，并且正确识别接头位点是基因组注释系统中的主要基石。

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《Bioinformatics》 |2018年第24期|共9页
作者
Zuallaert Jasper; Godin Frederic; Kim Mijung; Soete Arne; Saeys Yvan; De Neve Wesley;
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作者单位

Ghent Univ Global Campus Ctr Biotech Data Sci Dept Environm Technol Food Technol &

Mol Biotechn Incheon 305701 South Korea;

Univ Ghent Dept Elect &

Informat Syst IDLab B-9000 Ghent Belgium;

Ghent Univ Global Campus Ctr Biotech Data Sci Dept Environm Technol Food Technol &

Mol Biotechn Incheon 305701 South Korea;

Univ Ghent Dept Biomed Mol Biol Ghent Belgium;

VIB Inflammat Res Ctr Data Min &

Modeling Biomed Ghent Belgium;

Ghent Univ Global Campus Ctr Biotech Data Sci Dept Environm Technol Food Technol &

Mol Biotechn Incheon 305701 South Korea;

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正文语种 eng
中图分类生物工程学（生物技术）;
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专利

1. SpliceRover: interpretable convolutional neural networks for improved splice site prediction [J] . Zuallaert Jasper, Godin Frederic, Kim Mijung, Bioinformatics . 2018,第24期

机译：拼接器：可解释的卷积神经网络，用于改进拼接网站预测
2. Protein secondary structure prediction improved by recurrent neural networks integrated with two-dimensional convolutional neural networks [J] . Guo Yanbu, Wang Bingyi, Li Weihua, Journal of Bioinformatics and Computational Biology . 2018,第5期

机译：通过与二维卷积神经网络集成的经常性神经网络改善了蛋白质二级结构预测
3. Loss-Based Attention for Interpreting Image-Level Prediction of Convolutional Neural Networks [J] . Xiaoshuang Shi, Fuyong Xing, Kaidi Xu, IEEE Transactions on Image Processing . 2021,第1期

机译：基于丢失的注意力解释卷积神经网络的图像级预测
4. Interpretable convolutional neural networks for effective translation initiation site prediction [C] . Jasper Zuallaert, Mijung Kim, Yvan Saeys, IEEE International Conference on Bioinformatics and Biomedicine . 2017

机译：可解释的卷积神经网络，用于有效的翻译起始位点预测
5. A Unified Framework based on Convolutional Neural Networks for Interpreting Carotid Intima-Media Thickness Videos [D] . Shin, Jaeyul 2016

机译：基于卷积神经网络的统一框架，用于解释颈动脉内膜介质厚度视频
6. Splice2Deep: An ensemble of deep convolutional neural networks for improved splice site prediction in genomic DNA [O] . Somayah Albaradei, Arturo Magana-Mora, Maha Thafar, 2020

机译：Splice2Deep：深度卷积神经网络的集合用于改善基因组DNA中的剪接位点预测
7. SpliceRover: interpretable convolutional neural networks for improved splice site prediction [O] . Jasper Zuallaert, Fréderic Godin, Mijung Kim, 2018

机译：拼接器：可解释的卷积神经网络，用于改进拼接网站预测

SpliceRover: interpretable convolutional neural networks for improved splice site prediction

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