Bidirectional Long Short-Term Memory Networks for predicting the subcellular localization of eukaryotic proteins.
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 percent novel non-plant 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 (http://www.stepc.gr/~synaptic/blstm.html).
Trias
T
Thireou
Martin
M
Reczko
D000465:Algorithms; D000595:Amino Acid Sequence; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D020543:Proteome / Q000737:chemistry* / Q000378:metabolism*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D013329:Structure-Activity Relationship; D013347:Subcellular Fractions / Q000378:metabolism*
D020543:Proteome