Gene

Quantum mechanics

microRNA

Algorithm

Biology

Identification (biology)

Factorization

Similarity (geometry)

Eigenvalues and eigenvectors

Data mining

Computer science

Botany

Computational biology

Non-negative matrix factorization

Genetics

Physics

Image (mathematics)

Artificial intelligence

Matrix decomposition

Logistic regression

Machine learning

Knowledge-Based Systems

Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) interactions play important roles in diagnostic biomarkers and therapeutic targets for various human diseases. However, experimental methods for finding miRNAs associated with a particular lncRNA are costly, time consuming, and only a few theoretical approaches play a role in predicting potential lncRNA–miRNA associations. In this study, we have established a novel matrix factorization model to predict lncRNA–miRNA interactions, namely lncRNA–miRNA interactions prediction by logistic matrix factorization with neighborhood regularized (LMFNRLMI). Meanwhile, it only utilizes known positive samples to mine potential associations in data that lack negative samples. As a result, this new model obtains reliable performance in the leave-one-out cross validation (the AUC of 0.9319) and 5-fold cross validation (the AUC of 0.9220), which has significantly improved performance in predicting potential lncRNA–miRNA associations compared to other models. Furthermore, comparison with several other network algorithms, and test based on all kinds of similarity, our model successfully confirms the superiority of LMFNRLMI. Whereby, we hope that LMFNRLMI can be a useful tool for potential lncRNA–miRNA association identification in the future.

Predicting lncRNA–miRNA interactions based on logistic matrix factorization with neighborhood regularized