Analysis of Gene Expression Time Series Data of Ebola Vaccine response using the NeuCube and Temporal Feature Selection

摘要

The purpose of this paper was to investigate a pipeline for processing temporal gene expression data using spiking neural networks and temporal feature selection techniques that would allow for genomic marker discovery. A promising temporal feature selection method was tested using the NeuCube for classification against a set of previously identified genes using a dataset from Ebola vaccine trials. Classification results from the temporal selection method and the NeuCube model were significantly better than when using previously published gene sets. The discovered gene markers and their corresponding gene interaction network (GIN) are also new and have not been published before. This demonstrates both the potential of the examined feature selection method, and how Spiking Neural Networks (SNN) can be used for time series modelling and the discovery of novel GIN's. Future work includes improving temporal feature selection methods for gene expression data, and refining the use of SNN's for time series analysis.