TCSRWRLD A novel target convergence set based random walk with restart for prediction of potential LncRNA-disease associations

     In recent years, lncRNAs (long-non-coding RNAs) have been proved to be closely related to the
occurrence and development of many serious diseases that are seriously harmful to human health. However, most
of the lncRNA-disease associations have not been found yet due to high costs and time complexity of traditional
bio-experiments. Hence, it is quite urgent and necessary to establish efficient and reasonable computational models
to predict potential associations between lncRNAs and diseases.
     In this manuscript, a novel prediction model called TCSRWRLD is proposed to predict potential lncRNAdisease
associations based on improved random walk with restart. In TCSRWRLD, a heterogeneous lncRNA-disease
network is constructed first by combining the integrated similarity of lncRNAs and the integrated similarity of
diseases. And then, for each lncRNA/disease node in the newly constructed heterogeneous lncRNA-disease
network, it will establish a node set called TCS (Target Convergence Set) consisting of top 100 disease/lncRNA
nodes with minimum average network distances to these disease/lncRNA nodes having known associations with
itself. Finally, an improved random walk with restart is implemented on the heterogeneous lncRNA-disease network
to infer potential lncRNA-disease associations. The major contribution of this manuscript lies in the introduction of
the concept of TCS, based on which, the velocity of convergence of TCSRWRLD can be quicken effectively, since
the walker can stop its random walk while the walking probability vectors obtained by it at the nodes in TCS
instead of all nodes in the whole network have reached stable state. And Simulation results show that TCSRWRLD
can achieve a reliable AUC of 0.8712 in the Leave-One-Out Cross Validation (LOOCV), which outperforms previous
state-of-the-art results apparently. Moreover, case studies of lung cancer and leukemia demonstrate the satisfactory
prediction performance of TCSRWRLD as well.
       Both comparative results and case studies have demonstrated that TCSRWRLD can achieve excellent
performances in prediction of potential lncRNA-disease associations, which imply as well that TCSRWRLD may be a
good addition to the research of bioinformatics in the future.