id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-103077-sh4w2mye	Lu, Shuai	Leveraging Sequential and Spatial Neighbors Information by Using CNNs Linked With GCNs for Paratope Prediction	2020-10-16		.txt	text/plain	3140	194	55	In this article, we propose a method to identify which amino acid residues of an antibody directly interact with its associated antigen based on the features from sequence and structure. Our algorithm uses convolution neural networks (CNNs) linked with graph convolution networks (GCNs) to make use of information from both sequential and spatial neighbors to understand more about the local environment of the target amino acid residue. According to the type of selecting neighbors of target residue for representing and predicting, the machine learning-based methods can be divided into two categories, leveraging sequential neighbors or spatial neighbors. And the stateof-art method [19] represented an antibody as a graph where each amino acid residue was a node and K nearest spatial neighbors were used in the convolution operator. In this work, we utilize the sequential and spatial neighbors of the target antibody residue by using Convolutional Neural Networks (CNNs) linked with Graph Neural Networks (GCNs) for paratope prediction.	./cache/cord-103077-sh4w2mye.txt	./txt/cord-103077-sh4w2mye.txt