Gene essentiality prediction based on chaos game representation and spiking neural networks

Abstract

Abstract Chaos game representation (CGR) is a useful one-to-one visualization tool to represent nucleotide sequences, in which both local and global patterns of nucleotides can be graphically described. Deep learning networks have been proved to achieve outstanding performance on feature extraction and image recognition. In this paper, we use convolutional spiking neural networks (SNNs) with reward-modulated spike-timing-dependent plasticity (R-STDP) learning rule to learn from the frequency matrix chaos game representation (FCGR) images of essential and non-essential genes of 32 bacteria in the DEG database and make intra-organism and cross-organism essential gene predictions. For intra-organism predictions, our highest accuracy(ACC) score is 0.90 and the average ACC is 0.78, and for cross-organism predictions, our highest ACC is 0.79 and the average ACC is 0.68. Compared with the results of traditional machine learning classifiers training with FCGR images or numerical fractal features pre-calculated from CGR representations, our intra-organism prediction results are much better for all the bacteria or most bacteria, respectively, indicating that our spiking neural networks can make better essential gene prediction by extracting the gene features directly from the FCGR images of essential and nonessential genes. Compared with essential gene prediction methods using gene sequence features and topological features, our cross-organism prediction results can achieve performance close to or even better than such methods, while requiring much fewer input features.