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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

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/132232

Title:	PSLCNN: Protein Subcellular Localization Prediction for Eukaryotes and Prokaryotes Using Deep Learning
Authors:	張家銘 Chang, Jia-Ming Chang, Che-Yu Hsu, Tz-Wei
Contributors:	資科系
Keywords:	Deep learning;protein localization;convolutional neural networks
Date:	2019-11
Issue Date:	2020-10-27 13:48:28 (UTC+8)
Abstract:	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.
Relation:	International Conference on Technologies and Applications of Artificial Intelligence, Taiwanese Association for Artificial Intelligence
Data Type:	conference
Appears in Collections:	[資訊科學系] 會議論文

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