A Learning-Based Method for LncRNA-Disease Association Identification Combing Similarity Information and Rotation Forest

摘要

class="head no_bottom_margin" id="sec1title">IntroductionLong non-coding RNAs (lncRNAs) are an important class of transcripts, with the length longer than 200 nt, which participates in various physiological processes, such as immune surveillance, post-translational regulation, cell differentiation, proliferation, apoptosis, and epigenetic regulation. Especially, accumulating studies have indicated that a large number of lncRNAs are involved in numerous complex human diseases, such as various cancers (, ), blood diseases (, , ), and neurodegeneration diseases (). Therefore, inferring the potential association between lncRNA and disease is helpful to understand the pathogenesis of complex diseases at the molecular level and provide new insights into the diagnosis, treatment, and prognosis of diseases.Profit from the development of high-throughput experimental techniques, such as Microarray, Northern blots and qPCR, Fluorescence in situ hybridization, RNA interference, and RNA immunoprecipitation (), a large amount of data about lncRNAs-disease associations have been determined and distributed in different public databases, such as lncRNAdb (), NRED (), and NONCODE (). However, although experimentally validated lncRNA-disease associations drive research and development of medical molecular biology, they often have high false positives and false negatives. Moreover, many experimental methods are expensive and time-consuming. Consequently, it is essential to develop a computational prediction approach based on the accumulated biological data to accurately and rapidly find potential lncRNAs-disease associations. Computational method can quantitatively describe the associations between lncRNAs and diseases and efficiently screen out the most promising lncRNA-disease association pairs for further biological experimental validation.The proposed computational method for predicting lncRNA-disease association can be roughly divided into three categories. Methods in the first category uncover ncRNA-disease associations based on the idea of network or link prediction. The underlying assumption is that lncRNAs associated with the same or similar diseases are more likely to have similar functions. Liao et al. constructed a coding-non-coding gene co-expression network based on public microarray expression profiles to discover the potential functions of lncRNA (). Yang et al. applied a propagation algorithm to predict lncRNA-disease associations by constructing a coding-non-coding gene-disease bipartite network based on known associations between diseases and disease-causing genes (). Chen et al. came up with the model called IRWRLDA to identify potential associations by integrating known lncRNA-disease associations, disease semantic similarity, and various lncRNA similarity measures (). Huang et al. proposed a model called PBMDA to predict microRNA (miRNA)-disease associations by integrating known human miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity (). Methods in the second category utilize matrix factorization to identify potential lncRNA-disease associations. The basic assumption is that unknown association information can be derived from other known association information. Fu et al. predicted lncRNA-disease associations by decomposing data matrices of heterogeneous data sources into low-rank matrices (). Lu et al. developed a method called SIMCLDA for potential lncRNA-disease association prediction based on inductive matrix completion (href="#bib16" rid="bib16" class=" bibr popnode">Lu et al., 2018). These two types of methods are based on specific assumptions, but these assumptions are not unanimously accepted. Relevant studies have shown that in many cases bio macromolecules with similar structures or ligands do not have the same functions. Matrix factorization approaches will experience dramatic performance degradation when the known associated information is insufficient. In addition, these methods both cannot mine the similarity feature of lncRNA and disease, and consider the inherent logic of the association between lncRNA and disease from the perspective of data-driven. Machine learning models are used in the third category to discover the unknown lncRNA-disease associations. Lan et al. proposed a method called LDAP to identify latent associations between lncRNAs and diseases by using a bagging support vector machine (SVM) classifier based on lncRNA similarity and disease similarity (href="#bib13" rid="bib13" class=" bibr popnode">Lan et al., 2016). Since these methods are the beginning of machine learning application for lncRNA-disease association prediction, there is still much room for improvement in the prediction performance, prediction accuracy of such methods can be still greatly improved by increasing training samples and using more appropriate and advanced learning algorithms. Recently, the accumulation of association data between lncRNA and disease and the development of machine learning technology provide a better opportunity for predicting the association between lncRNA and disease using supervised learning model.Instead of using network-based and matrix factorization-based methods to compute association scores directly, we explored to extract association features from lncRNA-disease pairs by multiple similarity matrices and trained machine learning models in a supervised manner to predict their association. In this study, we proposed a novel supervised computational method named (LDASR) for large-scale lncRNA-disease association prediction based on collaborative filtering and machine learning technologies. First, the feature vectors of the lncRNA-disease pairs were obtained by integrating lncRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. Second, autoencoder neural network was employed to low the feature dimension and get the optimal feature subspace from the original feature set. Finally, considering the size of training samples and the possible non-linear relationship in input, we trained rotating forest to carry out prediction of LncRNA-Disease Association. The flow of LDASR is represented in href="/pmc/articles/PMC6733997/figure/fig1/" target="figure" class="fig-table-link figpopup" rid-figpopup="fig1" rid-ob="ob-fig1" co-legend-rid="lgnd_fig1">Figure 1. In leave-one-out cross-validation (LOOCV) and five cross-validation to evaluate test data, the proposed LDASR model achieved better results than some previous methods, with AUC of 0.9502 and 0.9428, respectively. The test results show that supervised learning model can achieve better performance.href="/pmc/articles/PMC6733997/figure/fig1/" target="figure" rid-figpopup="fig1" rid-ob="ob-fig1">class="inline_block ts_canvas" href="/core/lw/2.0/html/tileshop_pmc/tileshop_pmc_inline.html?title=Click%20on%20image%20to%20zoom&p=PMC3&id=6733997_gr1.jpg" target="tileshopwindow">target="object" href="/pmc/articles/PMC6733997/figure/fig1/?report=objectonly">Open in a separate windowclass="figpopup" href="/pmc/articles/PMC6733997/figure/fig1/" target="figure" rid-figpopup="fig1" rid-ob="ob-fig1">Figure 1Flowchart of LDASRStep 1: Building three similarity matrices for disease by combining semantic information and Gaussian kernel information. Step 2: Building 1 similarity matrix for lncRNA. Step 3: Extraction of similarity feature vectors for disease and lncRNA from disease similarity matrix and lncRNA similarity matrix. Step 4: Extracting the same number of positive and negative samples from the adjacency matrix to construct the dataset used in this paper. Step 5: Selecting the most valuable features and reducing feature noise by using autoencoder. Step 6: more discriminant feature vectors were put into Rotation Forest ensemble classifier for training, verification, and prediction. The construction of disease semantic matrix can see also href="#mmc1" rid="mmc1" class=" supplementary-material">Figure S1.