政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/132232
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    题名: PSLCNN: Protein Subcellular Localization Prediction for Eukaryotes and Prokaryotes Using Deep Learning
    作者: 張家銘
    Chang, Jia-Ming
    Chang, Che-Yu
    Hsu, Tz-Wei
    贡献者: 資科系
    关键词: Deep learning;protein localization;convolutional neural networks
    日期: 2019-11
    上传时间: 2020-10-27 13:48:28 (UTC+8)
    摘要: Many machine learning methods have been used to predict prokaryotic and eukaryotic protein subcellular localization. As most algorithms involve specific feature engineering, we carry out prediction using the feature-free property of deep learning methods. We present PSLCNN, a model using deep neural networks to predict protein subcellular localization for eukaryotes and prokaryotes. Only sequence information is needed (FASTA format). The model uses 1D convolution and predicts where the query localizes. It was trained and tested on an un-redundant dataset from the latest UniProt release, only for data with experimental annotation. Compared with the state-of-the-art tools, PSLCNN achieves the best performance for prokaryotes and is comparable for eukaryotes. We have also implemented a free PSLCNN web service available at https://github.com/changlabtw/PSLCNN.
    關聯: International Conference on Technologies and Applications of Artificial Intelligence, Taiwanese Association for Artificial Intelligence
    数据类型: conference
    显示于类别:[資訊科學系] 會議論文

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