id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-029030-3p0yieqv	Fan, Chunyan	Inferring Candidate CircRNA-Disease Associations by Bi-random Walk Based on CircRNA Regulatory Similarity	2020-06-22		.txt	text/plain	2360	153	49	In this study, we proposed a novel method named BWHCDA, which applied bi-random walk algorithm on the heterogeneous network for predicting circRNA-disease associations. Subsequently, the bi-random walk algorithm is implemented on the heterogeneous network to predict circRNA-disease associations. Finally, we utilize leave-one-out cross validation and 10-fold cross validation frameworks to evaluate the prediction performance of BWHCDA method and obtain AUC of 0.9334 and 0.8764 ± 0.0038, respectively. In this study, we developed a novel framework for forecasting circRNA-disease associations named BWHCDA, which integrated multiple similarity measures and implemented bi-random walk algorithm (Fig. 1) . First, circRNA regulatory similarity is effective measured based on circRNAs may play essential roles in regulating miRNA function in disease occurrence and progression. Then, circular bigraph (CBG) patterns are introduced in bi-random walk algorithm to predict the missing associations based on the heterogeneous network. Prediction of CircRNA-disease associations using KATZ model based on heterogeneous networks Predicting circRNA-disease associations based on circRNA expression similarity and functional similarity	./cache/cord-029030-3p0yieqv.txt	./txt/cord-029030-3p0yieqv.txt