期刊
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
卷 4, 期 3, 页码 441-446出版社
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2007.1015
关键词
recurrent neural networks; long short-term memory; biological sequence analysis; protein subcellular localization prediction
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- range 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 percent novel nonplant proteins and 88.4 percent 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 at http://stepc.stepc.gr/similar to synaptic/blstm.html.
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