Abstract
Accumulating experimental studies have demonstrated important associations between alterations and dysregulations of lncRNAs and the development and progression of various complex human diseases. Developing effective computational models to integrate vast amount of heterogeneous biological data for the identification of potential disease-lncRNA associations has become a hot topic in the fields of human complex diseases and lncRNAs, which could benefit lncRNA biomarker detection for disease diagnosis, treatment, and prevention. Considering the limitations in previous computational methods, the model of KATZ measure for LncRNA-Disease Association prediction (KATZLDA) was developed to uncover potential lncRNA-disease associations by integrating known lncRNA-disease associations, lncRNA expression profiles, lncRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. KATZLDA could work for diseases without known related lncRNAs and lncRNAs without known associated diseases. KATZLDA obtained reliable AUCs of 7175, 0.7886, 0.7719 in the local and global leave-one-out cross validation and 5-fold cross validation, respectively, significantly improving previous classical methods. Furthermore, case studies of colon, gastric, and renal cancer were implemented and 60% of top 10 predictions have been confirmed by recent biological experiments. It is anticipated that KATZLDA could be an important resource with potential values for biomedical researches.
Highlights
Some computational methods have been developed to predict novel disease-lncRNA associations, which could be classified into the following three categories
In the context of lncRNA-disease association prediction, KATZ measure for LncRNA-Disease Association prediction (KATZLDA) computes the similarity scores between candidate lncRNAs and investigated diseases by integrating walks of different lengths between corresponding lncRNA and disease nodes (See the Method section for the detail of KATZLDA) in the heterogeneous network consisting of known disease-lncRNA association network, disease similarity network, and lncRNA similarity network
293 distinct experimentally confirmed lncRNA–disease associations download from the LncRNADisease database were used as gold standard dataset in the cross validation for model evaluation and training dataset in the potential disease-lncRNA association prediction, respectively
Summary
Global and local LOOCV were implemented based on known experimentally verified lncRNA-disease associations in the lncRNADisease database to evaluate the performance of KATZLDA. When LOOCV was implemented, each known disease-lncRNA association was left out in turn as test sample and other known disease-lncRNA associations were regarded as training samples for model learning. Performance comparisons between KATZLD and three the-state-of-art disease-lncRNA association prediction models (LRLSLDA, RWRlncD, and NRWRH) in terms of ROC curve and AUC based on LOOCV. KATZLDA achieved AUCs of 0.7886 and 0.7175 for the global and local LOOCV, respectively, which significantly improved all the previous classical models and effectively demonstrated its reliable predictive ability the lncRNAs without known associations with this disease would be regarded as candidate samples. Global LOOCV can’t be implemented for RWRlncD and NRWRH because they only predict associated lncRNAs for the given disease. The comparison between KATZLDA and LRLSLDA based on 5-fold cross validation was implemented to further
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