id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_cjzncjfh4jgvffcb2oqjlenewi	Nguyen Quoc Khanh Le	SNARE-CNN: a 2D convolutional neural network architecture to identify SNARE proteins from high-throughput sequencing data	2019	17	.pdf	application/pdf	7150	862	61	study attempts to use deep learning to predict SNARE proteins, which is one of the SNARE-CNN model which uses two-dimensional convolutional neural networks and position-specific scoring matrix profiles could identify SNARE proteins with achieved for identifying SNARE proteins and a basis for further research that can apply deep Keywords Position specific scoring matrix, SNARE protein function, Deep learning, Membrane molecular functions; (iii) valid benchmark dataset to train and test SNARE proteins with Figure 1 Flowchart for identifying SNARE proteins using two-dimensional convolutional neural networks. We then propose a method to predict SNARE proteins by using their PSSM profiles as the model might predict SNARE proteins accurately via the special features from those amino Performance for identifying SNARE proteins with 2D CNN Table 3 Performance results of identifying SNAREs with different filter layers. Figure 3 The validation accuracy on identifying SNARE proteins using different optimizers. layers, nadam optimizer, and dropout value of 0.1 to identify SNARE proteins with the	./cache/work_cjzncjfh4jgvffcb2oqjlenewi.pdf	./txt/work_cjzncjfh4jgvffcb2oqjlenewi.txt