Convolutional LSTM Networks for Subcellular Localization of Proteins

TL;DR

This paper presents an advanced LSTM-based neural network model with convolutional and attention mechanisms for accurately predicting protein subcellular localization from sequences, offering new visualization tools for biological insights.

Contribution

It introduces a convolutional LSTM model with attention for protein localization prediction, outperforming existing methods and providing interpretability through novel visualizations.

Findings

Achieved 0.902 accuracy in localization prediction

Enhanced model performance with convolutional filters and attention

Provided visualizations for biological interpretation

Abstract

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.