Constructing lncRNA functional similarity network based on lncRNA-disease associations and disease semantic similarity
TLDR
Two novel lncRNA functional similarity calculation models (LNCSIM) are developed based on the assumption that functionally similar lncRNAs tend to be associated with similar diseases and it is anticipated that LNCSIM could be a useful and important biological tool for human disease diagnosis, treatment, and prevention.Abstract:
Increasing evidence has indicated that plenty of lncRNAs play important roles in many critical biological processes Developing powerful computational models to construct lncRNA functional similarity network based on heterogeneous biological datasets is one of the most important and popular topics in the fields of both lncRNAs and complex diseases Functional similarity network construction could benefit the model development for both lncRNA function inference and lncRNA-disease association identification However, little effort has been attempted to analysis and calculate lncRNA functional similarity on a large scale In this study, based on the assumption that functionally similar lncRNAs tend to be associated with similar diseases, we developed two novel lncRNA functional similarity calculation models (LNCSIM) LNCSIM was evaluated by introducing similarity scores into the model of Laplacian Regularized Least Squares for LncRNA-Disease Association (LRLSLDA) for lncRNA-disease association prediction As a result, new predictive models improved the performance of LRLSLDA in the leave-one-out cross validation of various known lncRNA-disease associations datasets Furthermore, some of the predictive results for colorectal cancer and lung cancer were verified by independent biological experimental studies It is anticipated that LNCSIM could be a useful and important biological tool for human disease diagnosis, treatment, and preventionread more
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Journal ArticleDOI
MicroRNAs and complex diseases: from experimental results to computational models.
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.
Journal ArticleDOI
Long non-coding RNAs and complex diseases: from experimental results to computational models
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.
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WBSMDA: Within and Between Score for MiRNA-Disease Association prediction.
Xing Chen,Chenggang Clarence Yan,Xu Zhang,Zhu-Hong You,Lixi Deng,Ying Liu,Yongdong Zhang,Qionghai Dai +7 more
TL;DR: The model of Within and Between Score for MiRNA-Disease Association prediction (WBSMDA) was developed to predict potential miRNAs associated with various complex diseases and would be a useful resource for potential miRNA-disease association identification.
Journal ArticleDOI
MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction.
TL;DR: A computational model of Matrix Decomposition and Heterogeneous Graph Inference for miRNAs association prediction (MDHGI) to discover new miRNA-disease associations by integrating the predicted association probability obtained from matrix decomposition through sparse learning method, the miRNA functional similarity, the disease semantic similarity, and the Gaussian interaction profile kernel similarity for diseases and mi RNAs into a heterogeneous network is developed.
Journal ArticleDOI
HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction
TL;DR: The computational model of Heterogeneous Graph Inference for MiRNA-Disease Association prediction (HGIMDA) is developed to uncover potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimentally verified miRNAs associations into a heterogeneous graph.
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