Abstract
BackgroundLong non-coding RNAs play an important role in human complex diseases. Identification of lncRNA-disease associations will gain insight into disease-related lncRNAs and benefit disease diagnoses and treatment. However, using experiments to explore the lncRNA-disease associations is expensive and time consuming.ResultsIn this study, we developed a novel method to identify potential lncRNA-disease associations by Integrating Diverse Heterogeneous Information sources with positive pointwise Mutual Information and Random Walk with restart algorithm (namely IDHI-MIRW). IDHI-MIRW first constructs multiple lncRNA similarity networks and disease similarity networks from diverse lncRNA-related and disease-related datasets, then implements the random walk with restart algorithm on these similarity networks for extracting the topological similarities which are fused with positive pointwise mutual information to build a large-scale lncRNA-disease heterogeneous network. Finally, IDHI-MIRW implemented random walk with restart algorithm on the lncRNA-disease heterogeneous network to infer potential lncRNA-disease associations.ConclusionsCompared with other state-of-the-art methods, IDHI-MIRW achieves the best prediction performance. In case studies of breast cancer, stomach cancer, and colorectal cancer, 36/45 (80%) novel lncRNA-disease associations predicted by IDHI-MIRW are supported by recent literatures. Furthermore, we found lncRNA LINC01816 is associated with the survival of colorectal cancer patients. IDHI-MIRW is freely available at https://github.com/NWPU-903PR/IDHI-MIRW.
Highlights
Long non-coding RNAs play an important role in human complex diseases
To address the aforementioned issues and further improve the prediction accuracy, we proposed a novel network-based method, namely IDHI-MIRW, to predict the potential Long non-coding RNAs (lncRNAs)-disease associations by constructing a large-scale lncRNA-disease heterogeneous network with Random Walk with Restart (RWR) algorithm and the positive pointwise mutual information (PPMI)
The results of IDHI-MIRW on HNetS and HNetL heterogeneous networks in leave-one-out cross validation (LOOCV) test are listed in Table 2, from which we can see that introducing more lncRNAs and diseases can effectively improve the predictive performance of IDHI-MIRW and can predict the potential lncRNAs/diseases for new disease/ lncRNA without any known disease/lncRNA association information
Summary
Long non-coding RNAs play an important role in human complex diseases. Identification of lncRNAdisease associations will gain insight into disease-related lncRNAs and benefit disease diagnoses and treatment. Computational methods have been developed to predict the potential lncRNA-disease associations that can be used as candidates for biological experiment verifications, which would greatly reduce the experiment cost and save time for finding new lncRNA-disease associations. The machine learning-based methods, such as LRLSLDA [18], LDAP [26], and MFLDA [27], have been developed to predict the potential lncRNA-disease associations. LDAP [26] employed two lncRNA similarity measures and five disease similarity measures to calculate lncRNA similarities and disease similarities, respectively, used the bagging SVM to predict lncRNA-disease associations This method suffered from fusing multiple similarities effectively. MFLDA can only predict the potential lncRNA-disease associations which share both lncRNAs and diseases with known associations in training set
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