Abstract
Long non-coding RNAs (lncRNAs) constitute a large class of transcribed RNA molecules. They have a characteristic length of more than 200 nucleotides which do not encode proteins. They play an important role in regulating gene expression by interacting with the homologous RNA-binding proteins. Due to the laborious and time-consuming nature of wet experimental methods, more researchers should pay great attention to computational approaches for the prediction of lncRNA-protein interaction (LPI). An in-depth literature review in the state-of-the-art in silico investigations, leads to the conclusion that there is still room for improving the accuracy and velocity. This paper propose a novel method for identifying LPI by employing Kernel Ridge Regression, based on Fast Kernel Learning (LPI-FKLKRR). This approach, uses four distinct similarity measures for lncRNA and protein space, respectively. It is remarkable, that we extract Gene Ontology (GO) with proteins, in order to improve the quality of information in protein space. The process of heterogeneous kernels integration, applies Fast Kernel Learning (FastKL) to deal with weight optimization. The extrapolation model is obtained by gaining the ultimate prediction associations, after using Kernel Ridge Regression (KRR). Experimental outcomes show that the ability of modeling with LPI-FKLKRR has extraordinary performance compared with LPI prediction schemes. On benchmark dataset, it has been observed that the best Area Under Precision Recall Curve (AUPR) of 0.6950 is obtained by our proposed model LPI-FKLKRR, which outperforms the integrated LPLNP (AUPR: 0.4584), RWR (AUPR: 0.2827), CF (AUPR: 0.2357), LPIHN (AUPR: 0.2299), and LPBNI (AUPR: 0.3302). Also, combined with the experimental results of a case study on a novel dataset, it is anticipated that LPI-FKLKRR will be a useful tool for LPI prediction.
Highlights
Long non-coding RNAs constitute a large class of transcribed molecules
We first show a result of 5-fold cross validation, conduct an independent analyzing about performance of single kernel
We have proposed a novel prediction method for the prediction of Long non-coding RNAs (lncRNAs)-protein interactions by using Kernel Ridge Regression, combined with a multiple kernel learning approach (LPI-FKLKRR)
Summary
Long non-coding RNAs (lncRNAs) constitute a large class of transcribed molecules They have a characteristic length of more than 200 nucleotides which do not encode proteins (St Laurent et al, 2015). A most recent research found that, a kind of lncRNA named lnc-Lsm3b can refrain the activity of the receptor RIG-I, by the induction of viruses during the regulation of immune response (Jiang et al, 2018). This is consistent with previous studies which have proven that lncRNAs are playing potential roles in complex human diseases (Li et al, 2013). Due to the laborious and time-consuming nature of wet experimental methods in molecular biology, many state-of-theart computational researches have been carried out dealing with the conundrum, in an effort to enhance accuracy and time efficiency (Zou et al, 2012; Jalali et al, 2015; Han et al, 2018)
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