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    政大機構典藏 > 資訊學院 > 資訊科學系 > 會議論文 >  Item 140.119/132232


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    題名: 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
    貢獻者: 資科系
    關鍵詞: Deep learning;protein localization;convolutional neural networks
    日期: 2019-11
    上傳時間: 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
    資料類型: conference
    顯示於類別:[資訊科學系] 會議論文

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