Abstract
Current studies have shown that long non-coding RNAs (lncRNAs) play a crucial role in a variety of fundamental biological processes related to complex human diseases. The prediction of latent disease-lncRNA associations can help to understand the pathogenesis of complex human diseases at the level of lncRNA, which also contributes to the detection of disease biomarkers, and the diagnosis, treatment, prognosis and prevention of disease. Nevertheless, it is still a challenging and urgent task to accurately identify latent disease-lncRNA association. Discovering latent links on the basis of biological experiments is time-consuming and wasteful, necessitating the development of computational prediction models. In this study, a computational prediction model has been remodeled as a matrix completion framework of the recommendation system by completing the unknown items in the rating matrix. A novel method named faster randomized matrix completion for latent disease-lncRNA association prediction (FRMCLDA) has been proposed by virtue of improved randomized partial SVD (rSVD-BKI) on a heterogeneous bilayer network. First, the correlated data source and experimentally validated information of diseases and lncRNAs are integrated to construct a heterogeneous bilayer network. Next, the integrated heterogeneous bilayer network can be formalized as a comprehensive adjacency matrix which includes lncRNA similarity matrix, disease similarity matrix, and disease-lncRNA association matrix where the uncertain disease-lncRNA associations are referred to as blank items. Then, a matrix approximate to the original adjacency matrix has been designed with predicted scores to retrieve the blank items. The construction of the approximate matrix could be equivalently resolved by the nuclear norm minimization. Finally, a faster singular value thresholding algorithm with a randomized partial SVD combing a new sub-space reuse technique has been utilized to complete the adjacency matrix. The results of leave-one-out cross-validation (LOOCV) experiments and 5-fold cross-validation (5-fold CV) experiments on three different benchmark databases have confirmed the availability and adaptability of FRMCLDA in inferring latent relationships of disease-lncRNA pairs, and in inferring lncRNAs correlated with novel diseases without any prior interaction information. Additionally, case studies have shown that FRMCLDA is able to effectively predict latent lncRNAs correlated with three widespread malignancies: prostate cancer, colon cancer, and gastric cancer.
Highlights
Long non-coding RNAs are RNA molecules whose transcripts are not less than 200 nucleotides, including intronic/exonic lncRNAs, antisense lncRNAs, overlapping lncRNA and long intergenic ncRNAs
With the development of the next-generation sequencing in biomedical research, constructing a heterogeneous network on the basis of clinical NGS big data will benefit in prediction models of latent human disease-lncRNA associations
Construction of computational prediction models for new disease-lncRNA relationships will help understand the molecular mechanism of complicated human diseases at the level of lncRNA, and recognize the disease biomarker for diagnosis, treatment, prognosis and prevention of disease
Summary
Long non-coding RNAs are RNA molecules whose transcripts are not less than 200 nucleotides, including intronic/exonic lncRNAs, antisense lncRNAs, overlapping lncRNA and long intergenic ncRNAs (lincRNAs). LncRNAs have long been considered as transcriptional noise, because of their absence in encoding proteins. It has been found that some lncRNAs regulate the expression of target genes after transcription, whose malfunction may lead to a number of diseases. Abnormal lncRNA expression may be involved in certain stages of cancer progression, which can serve as a potential biomarker for early tumor diagnosis (Zhou et al, 2015; Niknafs et al, 2016). LncRNAs are found able to interact with signaling pathways involved in the pathology of malignancy (Bian et al, 2015). Studies on the prediction of relationships between lncRNAs and diseases are still limited in number. One key bottleneck is the high cost and labor-intensity of laboratory techniques in discovering the relationships between lncRNAs and diseases. A lot of computational models have been proposed which can generally be divided into two major categories depending on the source of the interaction data: models for single-interaction data sources and models for multi-interaction data sources
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