Convolutional LSTM Networks for Subcellular Localization of Proteins
This improves protein localization prediction for bioinformatics, though it is incremental as it builds on existing LSTM methods.
The paper tackled the problem of predicting subcellular localization of proteins from sequences, achieving high accuracy (0.902) and outperforming state-of-the-art algorithms.
Machine learning is widely used to analyze biological sequence data. Non-sequential models such as SVMs or feed-forward neural networks are often used although they have no natural way of handling sequences of varying length. Recurrent neural networks such as the long short term memory (LSTM) model on the other hand are designed to handle sequences. In this study we demonstrate that LSTM networks predict the subcellular location of proteins given only the protein sequence with high accuracy (0.902) outperforming current state of the art algorithms. We further improve the performance by introducing convolutional filters and experiment with an attention mechanism which lets the LSTM focus on specific parts of the protein. Lastly we introduce new visualizations of both the convolutional filters and the attention mechanisms and show how they can be used to extract biological relevant knowledge from the LSTM networks.