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

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).

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Index Terms

• Applied computing

  • Life and medical sciences

    • Computational biology

    • Genetics

    • Systems biology

• Computing methodologies

  • Machine learning

    • Learning paradigms

    • Machine learning approaches

      • Markov decision processes

      • Neural networks

• Theory of computation

  • Theory and algorithms for application domains

    • Machine learning theory

      • Markov decision processes