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A graphical web tool to compute and visualize putative miRNA response elements in long non-coding RNAs (lncRNAs), along with different measures to assess their likely behavior as competitive endogenous RNAs. The algorithm is based on sequence complementarity and allows users to fix parameters to allow flexible search. The possibility of adding expression data to the prediction representation in the web tool, greatly facilitates downstream functional analysis. spongeScan differs from other lncRNA–miRNA interactions prediction sites that utilize CLIP-seq data in allowing massive searchers on user provided data and in being available for any organism with sequence information.
Infers long non-coding RNA (lncRNA) functional similarity (LFS) by modeling the information flow in an integrated network that comprises both lncRNA-related transcriptional and post-transcriptional information. IntNetLncSim integrated network involves lncRNA regulatory network, miRNA-mRNA interaction network, and mRNA-mRNA interaction network. The performance of IntNetLncSim was evaluated by investigating the relationship of LFS with the similarity of lncRNA-related mRNA sets (LmRSets) and miRNA sets (LmiRSets). Furthermore, highly-ranked lncRNA-disease associations confirmed by literature mining demonstrated the performance of IntNetLncSim. A web-accessible system was provided for querying LFS and potential lncRNA-disease relationships.
Explores lncRNA–RNA interactions by finding the minimum free energy joint structure of two RNA molecules based on base pairing. LncTar overwhelms the existing RNA–RNA prediction tools on the following aspects: (i) LncTar does not have a limit to RNA size and can process all length of current RNA molecules; (ii) this tool provides a quantitative standard to automatically determine whether two RNA molecules interact with each other. LncTar takes account of multiple binding sites using a matching algorithm, which finds the region of the minimum free energy joint structure between the input RNA sequences.
Allows prediction of human long non-coding RNA (lncRNA)-protein interactions. HLPI-Ensemble is a web application that enables production of negative samples for lncRNA-protein interactions by applying a random pairing strategy. Furthermore, it employs three mainstream machine learning algorithm of support vector machines (SVM), random forests (RF), and extreme gradient boosting by ensemble strategy to generate HLPI-SVM Ensemble, HLPI-RF Ensemble, and HLPI-RF Ensemble.
Lncin / lncRNA interaction
Identifies long non-coding RNAs (lncRNAs)-associated modules from protein interaction networks and predicts the function of lncRNAs based on the protein functions in the modules. Lncin utilizes not only the lncRNA-mRNA co-expression networks based on the rank of correlation which is a better measure of similarity than the correlation value, but also protein-protein interactions among co-expressed mRNAs to identify a set of mRNAs that may be modulated by lncRNA.
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