NDLI: Bidirectional Long Short-Term Memory Networks for Predicting the Subcellular Localization of Eukaryotic Proteins

Content Provider	ACM Digital Library
Author	Reczko, Martin Thireou, Trias
Abstract	An algorithm called Bidirectional Long Short-Term Memory Networks (BLSTM) for processing sequential data is introduced. This supervised learning method trains a special recurrent neural network to use very long ranged symmetric sequence context using a combination of nonlinear processing elements and linear feedback loops for storing long-range context. The algorithm is applied to the sequence-based prediction of protein localization and predicts 93.3% novel non-plant proteins and 88.4% novel plant proteins correctly, which is an improvement over feedforward and standard recurrent networks solving the same problem. The BLSTM system is available as a web-service (http://www.stepc.gr/~synaptic/blstm.html).
Starting Page	441
Ending Page	446
Page Count	6
File Format	PDF
ISSN	15455963
DOI	10.1109/tcbb.2007.1015
Volume Number	4
Issue Number	3
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2007-07-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Recurrent neural networks, long shortterm memory, biological sequence analysis, protein subcellular localization prediction
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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Bidirectional Long Short-Term Memory Networks for Predicting the Subcellular Localization of Eukaryotic Proteins