Xing Chen

TL;DR: The assumption that similar diseases tend to be associated with functionally similar lncRNAs is proposed and the method of Laplacian Regularized Least Squares for LncRNA-Disease Association (LRLSLDA) is developed in the semisupervised learning framework, which could be an effective and important biological tool for biomedical research.

TL;DR: Twenty state-of-the-art computational models of predicting miRNA-disease associations from different perspectives are reviewed, including five feasible and important research schemas, and future directions for further development of computational models are summarized.

TL;DR: In this review, databases and web servers involved in drug-target identification and drug discovery are summarized, and some state-of-the-art computational models for drug- target interactions prediction, including network-based method, machine learning- based method and so on are introduced.

TL;DR: Network-based Random Walk with Restart on the Heterogeneous network (NRWRH) is developed to predict potential drug-target interactions on a large scale under the hypothesis that similar drugs often target similar target proteins and the framework of Random Walk.

TL;DR: Some state-of-the-art computational models are introduced, which could be effectively used to identify disease-related lncRNAs on a large scale and select the most promising disease- related lnc RNAs for experimental validation and discussed the future directions of developing computational models for lncRNA research.