Long non-coding RNA disease association detection software tools | Non-coding RNA data analysis
Increasing evidences have demonstrated that long noncoding RNAs (lncRNAs) play important roles in many human diseases. Therefore, predicting novel lncRNA-disease associations would contribute to dissect the complex mechanisms of disease pathogenesis.
Investigates functions and mechanisms of long non-coding RNAs (lncRNAs) in cancer context. TANRIC is an application organized through three main panels for (i) exploring lncRNAs of interest according to their interactions with other TCGA's data types within and across tumor types; (ii) corroborating a pattern of interest or determining model cell lines for experimental identification; (iii) interrogating expression profiles of user-defined lncRNAs.
A computational model in the semisupervised learning framework. LRLSLDA method prioritizes the entire long-non-coding RNAome (lncRNAome) for disease of interest by integrating known phenome-lncRNAome network obtained from the database of LncRNADisease, disease similarity network and lncRNA similarity network. LRLSLDA is a global approach that can rank candidate disease-lncRNA pairs for all the diseases simultaneously. In the leave-one-out cross validation (LOOCV), LRLSLDA obtained the reliable AUC of 0.7760, demonstrating superiority performance of LRLSLDA to previous methods and potential value for disease-related lncRNA prediction and biomarker detection in the diagnosis, treatment, prognosis and prevention of human disease.
Quantifies long non-coding RNAs (lncRNA) functional similarity by combining known disease directed acyclic graphs (DAGs) and known lncRNA-disease associations. ILNCSIM is implemented as a web server that provides similarity calculation function for novel lncRNAs with associated diseases provided by users. It computes the functional similarity between this query lncRNA and all lncRNAs in two databases for a given lncRNA with the associated diseases.
Allows users to foresee novel lncRNA-disease associations. IRLWRLDA integrates various lncRNA similarity measures, disease semantic similarity, and known lncRNA-disease associations. It incorporates lncRNA expression similarity and disease semantic similarity to set the initial probability vector of Random walk with restart (RWR). This tool can be applied to any diseases without known related lncRNAs.
A calculation model based on the assumption that functionally similar long noncoding RNAs (lncRNAs) tend to be associated with similar diseases. FMLNCSIM performance improvement comes from the combination of information content and the concept of fuzzy measure, which was applied to the directed acyclic graphs of disease MeSH descriptors. FMLNCSIM web server mainly carries out four functions. The function 1 and 2 enable visitors obtain functional similarities calculated by FMLNCSIM model based on two lncRNA-disease association databases (i.e. LncRNADisease and MNDR). The function 3 and 4 provide functional similarity calculation for new lncRNAs as long as users provided its associated diseases. When visitors provide a specific lncRNA with its associated diseases, function 3 and 4 could calculate the functional similarities between this query lncRNA and all lncRNAs in LncRNADisease and MNDR databases, and then list the results on the webpage.
Predicts potential long non-coding RNA (lncRNA)-disease associations by integrating known microRNA (miRNA)-disease associations and lncRNA-miRNA interactions. HGLDA obtained a reliable area under ROC curve (AUC) of 0.7621 in the leave-one-out cross validation (LOOCV), based on known experimentally verified lncRNA-disease associations from the LncRNADisease database. It was also applied to predict: breast cancer, lung cancer, and colorectal cancer-related lncRNAs.
Uses a tripartite network to guide the inference process of novel ncRNA-disease associations. The tripartite network allows the introduction of two levels of interaction: ncRNA-target and target-disease. ncPred can exploit the greater quantity of known interactions between targets (i.e., proteins and miRNAs) and diseases to build a wider knowledge base and obtain a greater number of high quality predictions.