KATZLDA: KATZ measure for the lncRNA-disease association prediction

Similarity-guided graph contrastive learning for lncRNA-disease association prediction

The increasing research evidence indicates that long non-coding RNAs (lncRNAs) play important roles in regulating biological processes and are closely associated with many human diseases. Computational methods have emerged as indispensable tools for identifying associations between long non-coding RNA (lncRNA) and diseases, primarily due to the time-consuming and costly nature of traditional biological experiments. Given the scarcity of verified lncRNA-disease associations, the intensifying focus on deep learning is playing a crucial role in refining the accuracy of predictive models. Moreover, the contrastive learning method exhibits a clear advantage in situations where data is scarce or annotation costs are high. In this paper, we leverage the advantages of graph neural networks and contrastive learning to innovatively propose a similarity-guided graph contrastive learning (SGGCL) model for predicting lncRNA-disease associations. In the SGGCL model, we employ a novel similarity-guided graph data augmentation method to generate high-quality positive and negative sample pairs, addressing the scarcity of verified data. Additionally, we utilize the RWR algorithm and a graph convolutional neural network for contrastive learning, facilitating the capture of global topology and high-level node embeddings. The experimental results on several datasets demonstrate the superior predictive performance and scalability of our method in lncRNA-disease association prediction compared to state-of-the-art methods.

Finding potential lncRNA-disease associations using a boosting-based ensemble learning model

Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiments for identifying these associations are costly and laborious. Methods: We developed LDA-SABC, a novel boosting-based framework for lncRNA-disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) and classifies lncRNA-disease pairs (LDPs) by incorporating LightGBM and AdaBoost into the convolutional neural network. Results: The LDA-SABC performance was evaluated under five-fold cross validations (CVs) on lncRNAs, diseases, and LDPs. It obviously outperformed four other classical LDA inference methods (SDLDA, LDNFSGB, LDASR, and IPCAF) through precision, recall, accuracy, F1 score, AUC, and AUPR. Based on the accurate LDA prediction performance of LDA-SABC, we used it to find potential lncRNA biomarkers for lung cancer. The results elucidated that 7SK and HULC could have a relationship with non-small-cell lung cancer (NSCLC) and lung adenocarcinoma (LUAD), respectively. Conclusion: We hope that our proposed LDA-SABC method can help improve the LDA identification.

PDDGCN: A Parasitic Disease-Drug Association Predictor Based on Multi-view Fusion Graph Convolutional Network

The precise identification of associations between diseases and drugs is paramount for comprehending the etiology and mechanisms underlying parasitic diseases. Computational approaches are highly effective in discovering and predicting disease-drug associations. However, the majority of these approaches primarily rely on link-based methodologies within distinct biomedical bipartite networks. In this study, we reorganized a fundamental dataset of parasitic disease-drug associations using the latest databases, and proposed a prediction model called PDDGCN, based on a multi-view graph convolutional network. To begin with, we fused similarity networks with binary networks to establish multi-view heterogeneous networks. We utilized neighborhood information aggregation layers to refine node embeddings within each view of the multi-view heterogeneous networks, leveraging inter- and intra-domain message passing to aggregate information from neighboring nodes. Subsequently, we integrated multiple embeddings from each view and fed them into the ultimate discriminator. The experimental results demonstrate that PDDGCN outperforms five state-of-the-art methods and four compared machine learning algorithms. Additionally, case studies have substantiated the effectiveness of PDDGCN in identifying associations between parasitic diseases and drugs. In summary, the PDDGCN model has the potential to facilitate the discovery of potential treatments for parasitic diseases and advance our comprehension of the etiology in this field. The source code is available at https://github.com/AhauBioinformatics/PDDGCN .

HRGCNLDA: Forecasting of lncRNA-disease association based on hierarchical refinement graph convolutional neural network

Long non-coding RNA (lncRNA) is considered to be a crucial regulator involved in various human biological processes, including the regulation of tumor immune checkpoint proteins. It has great potential as both a cancer biomolecular biomarker and therapeutic target. Nevertheless, conventional biological experimental techniques are both resource-intensive and laborious, making it essential to develop an accurate and efficient computational method to facilitate the discovery of potential links between lncRNAs and diseases. In this study, we proposed HRGCNLDA, a computational approach utilizing hierarchical refinement of graph convolutional neural networks for forecasting lncRNA-disease potential associations. This approach effectively addresses the over-smoothing problem that arises from stacking multiple layers of graph convolutional neural networks. Specifically, HRGCNLDA enhances the layer representation during message propagation and node updates, thereby amplifying the contribution of hidden layers that resemble the ego layer while reducing discrepancies. The results of the experiments showed that HRGCNLDA achieved the highest AUC-ROC (area under the receiver operating characteristic curve, AUC for short) and AUC-PR (area under the precision versus recall curve, AUPR for short) values compared to other methods. Finally, to further demonstrate the reliability and efficacy of our approach, we performed case studies on the case of three prevalent human diseases, namely, breast cancer, lung cancer and gastric cancer.

Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation

Non-coding RNAs (ncRNAs), generated from nonprotein coding DNA sequences, constitute 98-99% of the human genome. Non-coding RNAs encompass diverse functional classes, including microRNAs, small interfering RNAs, PIWI-interacting RNAs, small nuclear RNAs, small nucleolar RNAs, and long non-coding RNAs. With critical involvement in gene expression and regulation across various biological and physiopathological contexts, such as neuronal disorders, immune responses, cardiovascular diseases, and cancer, non-coding RNAs are emerging as disease biomarkers and therapeutic targets. In this review, after providing an overview of non-coding RNAs' role in cell homeostasis, we illustrate the potential and the challenges of state-of-the-art computational methods exploited to study non-coding RNAs biogenesis, function, and modulation. This can be done by directly targeting them with small molecules or by altering their expression by targeting the cellular engines underlying their biosynthesis. Drawing from applications, also taken from our work, we showcase the significance and role of computer simulations in uncovering fundamental facets of ncRNA mechanisms and modulation. This information may set the basis to advance gene modulation tools and therapeutic strategies to address unmet medical needs.

GCNFORMER: graph convolutional network and transformer for predicting lncRNA-disease associations

BACKGROUND

A growing body of researches indicate that the disrupted expression of long non-coding RNA (lncRNA) is linked to a range of human disorders. Therefore, the effective prediction of lncRNA-disease association (LDA) can not only suggest solutions to diagnose a condition but also save significant time and labor costs.

METHOD

In this work, we proposed a novel LDA predicting algorithm based on graph convolutional network and transformer, named GCNFORMER. Firstly, we integrated the intraclass similarity and interclass connections between miRNAs, lncRNAs and diseases, and built a graph adjacency matrix. Secondly, to completely obtain the features between various nodes, we employed a graph convolutional network for feature extraction. Finally, to obtain the global dependencies between inputs and outputs, we used a transformer encoder with a multiheaded attention mechanism to forecast lncRNA-disease associations.

RESULTS

The results of fivefold cross-validation experiment on the public dataset revealed that the AUC and AUPR of GCNFORMER achieved 0.9739 and 0.9812, respectively. We compared GCNFORMER with six advanced LDA prediction models, and the results indicated its superiority over the other six models. Furthermore, GCNFORMER's effectiveness in predicting potential LDAs is underscored by case studies on breast cancer, colon cancer and lung cancer.

CONCLUSIONS

The combination of graph convolutional network and transformer can effectively improve the performance of LDA prediction model and promote the in-depth development of this research filed.

Graph Reasoning Method Based on Affinity Identification and Representation Decoupling for Predicting lncRNA-Disease Associations

An increasing number of studies have shown that dysregulation of lncRNAs is related to the occurrence of various diseases. Most of the previous methods, however, are designed based on homogeneity assumption that the representation of a target lncRNA (or disease) node should be updated by aggregating the attributes of its neighbor nodes. However, the assumption ignores the affinity nodes that are far from the target node. We present a novel prediction method, GAIRD, to fully leverage the heterogeneous information in the network and the decoupled node features. The first major innovation is a random walk strategy based on width-first searching and depth-first searching. Different from previous methods that only focus on homogeneous information, our new strategy learns both the homogeneous information within local neighborhoods and the heterogeneous information within higher-order neighborhoods. The second innovation is a representation decoupling module to extract the purer attributes and the purer topologies. Third, a module based on group convolution and deep separable convolution is developed to promote the pairwise intrachannel and interchannel feature learning. The experimental results show that GAIRD outperforms comparing state-of-the-art methods, and the ablation studies prove the contributions of major innovations. We also performed case studies on 3 diseases to further demonstrate the effectiveness of the GAIRD model in applications.

KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection

BACKGROUND

Clinical studies have shown that miRNAs are closely related to human health. The study of potential associations between miRNAs and diseases will contribute to a profound understanding of the mechanism of disease development, as well as human disease prevention and treatment. MiRNA-disease associations predicted by computational methods are the best complement to biological experiments.

RESULTS

In this research, a federated computational model KATZNCP was proposed on the basis of the KATZ algorithm and network consistency projection to infer the potential miRNA-disease associations. In KATZNCP, a heterogeneous network was initially constructed by integrating the known miRNA-disease association, integrated miRNA similarities, and integrated disease similarities; then, the KATZ algorithm was implemented in the heterogeneous network to obtain the estimated miRNA-disease prediction scores. Finally, the precise scores were obtained by the network consistency projection method as the final prediction results. KATZNCP achieved the reliable predictive performance in leave-one-out cross-validation (LOOCV) with an AUC value of 0.9325, which was better than the state-of-the-art comparable algorithms. Furthermore, case studies of lung neoplasms and esophageal neoplasms demonstrated the excellent predictive performance of KATZNCP.

CONCLUSION

A new computational model KATZNCP was proposed for predicting potential miRNA-drug associations based on KATZ and network consistency projections, which can effectively predict the potential miRNA-disease interactions. Therefore, KATZNCP can be used to provide guidance for future experiments.

Semantic Meta-Path Enhanced Global and Local Topology Learning for lncRNA-Disease Association Prediction

Since abnormal expression of long non-coding RNAs (lncRNAs) is associated with various human diseases, identifying disease-related lncRNAs helps reveal the pathogenesis of diseases. Existing methods for lncRNA-disease association prediction mainly focus on multi-sourced data related to lncRNAs and diseases. The rich semantic information of meta-paths, composed of multiple kinds of connections between lncRNA and disease nodes, is neglected. We propose a new prediction method, MGLDA, to encode and integrate the semantics of multiple meta-paths, the global topology of heterogeneous graph, and pairwise attributes of lncRNA and disease nodes. First, a tri-layer heterogeneous graph is constructed to associate multi-sourced data across the lncRNA, disease, and miRNA nodes. Afterwards, we establish multiple meta-paths connecting the lncRNA and disease nodes to derive and denote various semantics. Each meta-path contains its specific semantics formulated by an embedding strategy, and each embedding covers local topology formed by the diverse semantic connections among the lncRNA, disease, and miRNA nodes. We construct multiple graph convolutional autoencoders (GCA) with topology-level attention to learn global and multiple local topologies from the tri-layer graph and each meta-path, respectively. The topology-level attention mechanism can learn the importance of various global and local topologies for adaptive pairwise topology fusion. Finally, a convolutional autoencoder learns the attribute representations of lncRNA-disease pairs, which integrates the learnt detailed and representative pairwise features. Experimental results show that MGLDA outperforms other state-of-the-art prediction methods in comparison and retrieves more real lncRNA-disease associations in the top-ranked candidates. The ablation study also demonstrates the important contributions of the local and global topology learning, and pairwise attribute learning. Case studies on three diseases further demonstrate MGLDA's ability to identify potential disease-related lncRNAs.

Recent advances in predicting lncRNA-disease associations based on computational methods

Mutations in and dysregulation of long non-coding RNAs (lncRNAs) are closely associated with the development of various human complex diseases, but only a few lncRNAs have been experimentally confirmed to be associated with human diseases. Predicting new potential lncRNA-disease associations (LDAs) will help us to understand the pathogenesis of human diseases and to detect disease markers, as well as in disease diagnosis, prevention and treatment. Computational methods can effectively narrow down the screening scope of biological experiments, thereby reducing the duration and cost of such experiments. In this review, we outline recent advances in computational methods for predicting LDAs, focusing on LDA databases, lncRNA/disease similarity calculations, and advanced computational models. In addition, we analyze the limitations of various computational models and discuss future challenges and directions for development.

Screening potential lncRNA biomarkers for breast cancer and colorectal cancer combining random walk and logistic matrix factorization

Breast cancer and colorectal cancer are two of the most common malignant tumors worldwide. They cause the leading causes of cancer mortality. Many researches have demonstrated that long noncoding RNAs (lncRNAs) have close linkages with the occurrence and development of the two cancers. Therefore, it is essential to design an effective way to identify potential lncRNA biomarkers for them. In this study, we developed a computational method (LDA-RWLMF) by integrating random walk with restart and Logistic Matrix Factorization to investigate the roles of lncRNA biomarkers in the prognosis and diagnosis of the two cancers. We first fuse disease semantic and Gaussian association profile similarities and lncRNA functional and Gaussian association profile similarities. Second, we design a negative selection algorithm to extract negative LncRNA-Disease Associations (LDA) based on random walk. Third, we develop a logistic matrix factorization model to predict possible LDAs. We compare our proposed LDA-RWLMF method with four classical LDA prediction methods, that is, LNCSIM1, LNCSIM2, ILNCSIM, and IDSSIM. The results from 5-fold cross validation on the MNDR dataset show that LDA-RWLMF computes the best AUC value of 0.9312, outperforming the above four LDA prediction methods. Finally, we rank all lncRNA biomarkers for the two cancers after determining the performance of LDA-RWLMF, respectively. We find that 48 and 50 lncRNAs have the highest association scores with breast cancer and colorectal cancer among all lncRNAs known to associate with them on the MNDR dataset, respectively. We predict that lncRNAs HULC and HAR1A could be separately potential biomarkers for breast cancer and colorectal cancer and need to biomedical experimental validation.

HEGANLDA: A Computational Model for Predicting Potential Lncrna-Disease Associations Based On Multiple Heterogeneous Networks

Long non-coding RNAs (lncRNAs) play vital regulatory roles in many human complex diseases, however, the number of validated lncRNA-disease associations is notable rare so far. How to predict potential lncRNA-disease associations precisely through computational methods remains challenging. In this study, we proposed a novel method, LDVCHN (LncRNA-Disease Vector Calculation Heterogeneous Networks), and also developed the corresponding model, HEGANLDA (Heterogeneous Embedding Generative Adversarial Networks LncRNA-Disease Association), for predicting potential lncRNA-disease associations. In HEGANLDA, the graph embedding algorithm (HeGAN) was introduced for mapping all nodes in the lncRNA-miRNA-disease heterogeneous network into the low-dimensional vectors which severed as the inputs of LDVCHN. HEGANLDA effectively adopted the XGBoost (eXtreme Gradient Boosting) classifier, which was trained by the low-dimensional vectors, to predict potential lncRNA-disease associations. The 10-fold cross-validation method was utilized to evaluate the performance of our model, our model finally achieved an area under the ROC curve of 0.983. According to the experiment results, HEGANLDA outperformed any one of five current state-of-the-art methods. To further evaluate the effectiveness of HEGANLDA in predicting potential lncRNA-disease associations, both case studies and robustness tests were performed and the results confirmed its effectiveness and robustness. The source code and data of HEGANLDA are available at https://github.com/HEGANLDA/HEGANLDA.

lncRNA-disease association prediction method based on the nearest neighbor matrix completion model

State-of-the-art medical studies proved that long noncoding ribonucleic acids (lncRNAs) are closely related to various diseases. However, their large-scale detection in biological experiments is problematic and expensive. To aid screening and improve the efficiency of biological experiments, this study introduced a prediction model based on the nearest neighbor concept for lncRNA-disease association prediction. We used a new similarity algorithm in the model that fused potential associations. The experimental validation of the proposed algorithm proved its superiority over the available Cosine, Pearson, and Jaccard similarity algorithms. Satisfactory results in the comparative leave-one-out cross-validation test (with AUC = 0.96) confirmed its excellent predictive performance. Finally, the proposed model's reliability was confirmed by performing predictions using a new dataset, yielding AUC = 0.92.

Identifying lncRNA–disease association based on GAT multiple-operator aggregation and inductive matrix completion

Computable models as a fundamental candidate for traditional biological experiments have been applied in inferring lncRNA–disease association (LDA) for many years, without time-consuming and laborious limitations. However, sparsity inherently existing in known heterogeneous bio-data is an obstacle to computable models to improve prediction accuracy further. Therefore, a new computational model composed of multiple mechanisms for lncRNA–disease association (MM-LDA) prediction was proposed, based on the fusion of the graph attention network (GAT) and inductive matrix completion (IMC). MM-LDA has two key steps to improve prediction accuracy: first, a multiple-operator aggregation was designed in the n-heads attention mechanism of the GAT. With this step, features of lncRNA nodes and disease nodes were enhanced. Second, IMC was introduced into the enhanced node features obtained in the first step, and then the LDA network was reconstructed to solve the cold start problem when data deficiency of the entire row or column happened in a known association matrix. Our MM-LDA achieved the following progress: first, using the Adam optimizer that adaptively adjusted the model learning rate could increase the convergent speed and not fall into local optima as well. Second, more excellent predictive ability was achieved against other similar models (with an AUC value of 0.9395 and an AUPR value of 0.8057 obtained from 5-fold cross-validation). Third, a 6.45% lower time cost was consumed against the advanced model GAMCLDA. In short, our MM-LDA achieved a more comprehensive prediction performance in terms of prediction accuracy and time cost.

JSCSNCP-LMA: a method for predicting the association of lncRNA-miRNA

Non-coding RNAs (ncRNAs) have long been considered the "white elephant" on the genome because they lack the ability to encode proteins. However, in recent years, more and more biological experiments and clinical reports have proved that ncRNAs account for a large proportion in organisms. At the same time, they play a decisive role in the biological processes such as gene expression and cell growth and development. Recently, it has been found that short sequence non-coding RNA(miRNA) and long sequence non-coding RNA(lncRNA) can regulate each other, which plays an important role in various complex human diseases. In this paper, we used a new method (JSCSNCP-LMA) to predict lncRNA-miRNA with unknown associations. This method combined Jaccard similarity algorithm, self-tuning spectral clustering similarity algorithm, cosine similarity algorithm and known lncRNA-miRNA association networks, and used the consistency projection to complete the final prediction. The results showed that the AUC values of JSCSNCP-LMA in fivefold cross validation (fivefold CV) and leave-one-out cross validation (LOOCV) were 0.9145 and 0.9268, respectively. Compared with other models, we have successfully proved its superiority and good extensibility. Meanwhile, the model also used three different lncRNA-miRNA datasets in the fivefold CV experiment and obtained good results with AUC values of 0.9145, 0.9662 and 0.9505, respectively. Therefore, JSCSNCP-LMA will help to predict the associations between lncRNA and miRNA.

Geometric complement heterogeneous information and random forest for predicting lncRNA-disease associations

More and more evidences have showed that the unnatural expression of long non-coding RNA (lncRNA) is relevant to varieties of human diseases. Therefore, accurate identification of disease-related lncRNAs can help to understand lncRNA expression at the molecular level and to explore more effective treatments for diseases. Plenty of lncRNA-disease association prediction models have been raised but it is still a challenge to recognize unknown lncRNA-disease associations. In this work, we have proposed a computational model for predicting lncRNA-disease associations based on geometric complement heterogeneous information and random forest. Firstly, geometric complement heterogeneous information was used to integrate lncRNA-miRNA interactions and miRNA-disease associations verified by experiments. Secondly, lncRNA and disease features consisted of their respective similarity coefficients were fused into input feature space. Thirdly, an autoencoder was adopted to project raw high-dimensional features into low-dimension space to learn representation for lncRNAs and diseases. Finally, the low-dimensional lncRNA and disease features were fused into input feature space to train a random forest classifier for lncRNA-disease association prediction. Under five-fold cross-validation, the AUC (area under the receiver operating characteristic curve) is 0.9897 and the AUPR (area under the precision-recall curve) is 0.7040, indicating that the performance of our model is better than several state-of-the-art lncRNA-disease association prediction models. In addition, case studies on colon and stomach cancer indicate that our model has a good ability to predict disease-related lncRNAs.

lncRNA-disease association prediction based on matrix decomposition of elastic network and collaborative filtering

In recent years, with the continuous development and innovation of high-throughput biotechnology, more and more evidence show that lncRNA plays an essential role in biological life activities and is related to the occurrence of various diseases. However, due to the high cost and time-consuming of traditional biological experiments, the number of associations between lncRNAs and diseases that rely on experiments to verify is minimal. Computer-aided study of lncRNA-disease association is an important method to study the development of the lncRNA-disease association. Using the existing data to establish a prediction model and predict the unknown lncRNA-disease association can make the biological experiment targeted and improve its accuracy of the biological experiment. Therefore, we need to find an accurate and efficient method to predict the relationship between lncRNA and diseases and help biologists complete the diagnosis and treatment of diseases. Most of the current lncRNA-disease association predictions do not consider the model instability caused by the actual data. Also, predictive models may produce data that overfit is not considered. This paper proposes a lncRNA-disease association prediction model (ENCFLDA) that combines an elastic network with matrix decomposition and collaborative filtering. This method uses the existing lncRNA-miRNA association data and miRNA-disease association data to predict the association between unknown lncRNA and disease, updates the matrix by matrix decomposition combined with the elastic network, and then obtains the final prediction matrix by collaborative filtering. This method uses the existing lncRNA-miRNA association data and miRNA-disease association data to predict the association of unknown lncRNAs with diseases. First, since the known lncRNA-disease association matrix is very sparse, the cosine similarity and KNN are used to update the lncRNA-disease association matrix. The matrix is then updated by matrix decomposition combined with an elastic net algorithm, to increase the stability of the overall prediction model and eliminate data overfitting. The final prediction matrix is then obtained through collaborative filtering based on lncRNA.Through simulation experiments, the results show that the AUC value of ENCFLDA can reach 0.9148 under the framework of LOOCV, which is higher than the prediction result of the latest model.

High expression of lncRNA PELATON serves as a risk factor for the incidence and prognosis of acute coronary syndrome

Atherosclerosis is the primary origin of acute coronary syndrome (ACS) diseases. Previous studies have shown that lncRNA plaque-enriched long noncoding RNA in atherosclerotic macrophage regulation (lncRNA PELATON) is a specific lncRNA in macrophage nuclei. This study aims to identify serum lncRNA PELATON as a biomarker for assessing the incidence and prognosis of ACS. Levels of serum lncRNA PELATON were detected by real-time polymerase chain reaction (RT–PCR) in patients with ACS and healthy individuals. The clinical significance of lncRNA PELATON in patients with ACS was assessed by analyzing receiver operating characteristic and survival curves. The serum levels of lncRNA PELATON in patients with ACS were significantly higher than those in healthy individuals. LncRNA PELATON expression was positively correlated with the expression levels of high sensitivity C-reactive protein (hs-CRP), cardiac troponin T (cTnT) and creatine kinase MB (CK-MB) (p < 0.05). LncRNA PELATON can be used as a potential diagnostic index with an AUC of 0.706 for unstable angina pectoris (UA), 0.782 for acute non-ST-segment elevation myocardial infarction (NSTEMI) and 0.900 for acute ST-segment elevation myocardial infarction (STEMI). The incidence of major cardiovascular events in patients with ACS with high lncRNA PELATON expression was higher than that in those with low lncRNA PELATON expression. However, the mortality between patients in the high and low lncRNA PELATON groups was not significantly different. This study showed that higher levels of lncRNA PELATON were negatively correlated with the prognosis of ACS, revealing the potential of this measurement to serve as an index to assess the incidence and prognosis of ACS.

iLncDA-LTR: Identification of lncRNA-disease associations by learning to rank

Identifying the associations between lncRNAs and diseases is helpful for the treatment and diagnosis of complex diseases. The existing computational methods mainly focus on the identification of associations between known lncRNAs and known diseases. However, with the application of high-throughput sequencing in lncRNA research, more and more lncRNAs have been detected. Predicting diseases related with newly detected lncRNAs has not been fully explored. Therefore, there is an urgent need for developing powerful computational methods to predict diseases related with newly detected lncRNAs. In this paper, we propose a Learning to Rank (LTR)-based method called iLncDA-LTR to predict diseases related with newly detected lncRNAs. iLncDA-LTR treats this task as an information retrieval task. The newly detected lncRNAs and diseases are considered as queries and documents, respectively. For a given newly detected lncRNA (query), iLncDA-LTR integrates multiple relevant information into LTR for predicting candidate diseases associated with query lncRNA. Experimental results show that iLncDA-LTR outperforms the other exiting state-of-the-art predictors on independent dataset. The corresponding web server of iLncDA-LTR has been constructed as well (http://bliulab.net/iLncDA-LTR/).

NCP-BiRW: A Hybrid Approach for Predicting Long Noncoding RNA-Disease Associations by Network Consistency Projection and Bi-Random Walk

Long non-coding RNAs (lncRNAs) play significant roles in the disease process. Understanding the pathological mechanisms of lncRNAs during the course of various diseases will help clinicians prevent and treat diseases. With the emergence of high-throughput techniques, many biological experiments have been developed to study lncRNA-disease associations. Because experimental methods are costly, slow, and laborious, a growing number of computational models have emerged. Here, we present a new approach using network consistency projection and bi-random walk (NCP-BiRW) to infer hidden lncRNA-disease associations. First, integrated similarity networks for lncRNAs and diseases were constructed by merging similarity information. Subsequently, network consistency projection was applied to calculate space projection scores for lncRNAs and diseases, which were then introduced into a bi-random walk method for association prediction. To test model performance, we employed 5- and 10-fold cross-validation, with the area under the receiver operating characteristic curve as the evaluation indicator. The computational results showed that our method outperformed the other five advanced algorithms. In addition, the novel method was applied to another dataset in the Mammalian ncRNA-Disease Repository (MNDR) database and showed excellent performance. Finally, case studies were carried out on atherosclerosis and leukemia to confirm the effectiveness of our method in practice. In conclusion, we could infer lncRNA-disease associations using the NCP-BiRW model, which may benefit biomedical studies in the future.

Fully connected autoencoder and convolutional neural network with attention-based method for inferring disease-related lncRNAs

Since abnormal expression of long noncoding RNAs (lncRNAs) is often closely related to various human diseases, identification of disease-associated lncRNAs is helpful for exploring the complex pathogenesis. Most of recent methods concentrate on exploiting multiple kinds of data related to lncRNAs and diseases for predicting candidate disease-related lncRNAs. These methods, however, failed to deeply integrate the topology information from the meta-paths that are composed of lncRNA, disease and microRNA (miRNA) nodes. We proposed a new method based on fully connected autoencoders and convolutional neural networks, called ACLDA, for inferring potential disease-related lncRNA candidates. A heterogeneous graph that consists of lncRNA, disease and miRNA nodes were firstly constructed to integrate similarities, associations and interactions among them. Fully connected autoencoder-based module was established to extract the low-dimensional features of lncRNA, disease and miRNA nodes in the heterogeneous graph. We designed the attention mechanisms at the node feature level and at the meta-path level to learn more informative features and meta-paths. A module based on convolutional neural networks was constructed to encode the local topologies of lncRNA and disease nodes from multiple meta-path perspectives. The comprehensive experimental results demonstrated ACLDA achieves superior performance than several state-of-the-art prediction methods. Case studies on breast, lung and colon cancers demonstrated that ACLDA is able to discover the potential disease-related lncRNAs.

Revealing key lncRNAs in cytogenetically normal acute myeloid leukemia by reconstruction of the lncRNA-miRNA-mRNA network

Cytogenetically normal acute myeloid leukemia (CN-AML) is a heterogeneous disease with different prognoses. Researches on prognostic biomarkers and therapy targets of CN-AML are still ongoing. Instead of protein-coding genes, more and more researches were focused on the non-coding RNAs especially long non-coding RNAs (lncRNAs) which may play an important role in the development of AML. Although a large number of lncRNAs have been found, our knowledge of their functions and pathological process is still in its infancy. The purpose of this research is to identify the key lncRNAs and explore their functions in CN-AML by reconstructing the lncRNA-miRNA-mRNA network based on the competitive endogenous RNA (ceRNA) theory. We reconstructed a global triple network based on the ceRNA theory using the data from National Center for Biotechnology Information Gene Expression Omnibus and published literature. According to the topological algorithm, we identified the key lncRNAs which had both the higher node degrees and the higher numbers of lncRNA-miRNA pairs and total pairs in the ceRNA network. Meanwhile, Gene Ontology (GO) and pathway analysis were performed using databases such as DAVID, KOBAS and Cytoscape plug-in ClueGO respectively. The lncRNA-miRNA-mRNA network was composed of 90 lncRNAs,33mRNAs,26 miRNAs and 259 edges in the lncRNA upregulated group, and 18 lncRNAs,11 mRNAs,6 miRNAs and 45 edges in the lncRNA downregulated group. The functional assay showed that 53 pathways and 108 GO terms were enriched. Three lncRNAs (XIST, TUG1, GABPB1-AS1) could possibly be selected as key lncRNAs which may play an important role in the development of CN-AML. Particularly, GABPB1-AS1 was highly expressed in CN-AML by both bioinformatic analysis and experimental verification in AML cell line (THP-1) with quantitative real-time polymerase chain reaction. In addition, GABPB1-AS1 was also negatively correlated with overall survival of AML patients. The lncRNA-miRNA-mRNA network revealed key lncRNAs and their functions in CN-AML. Particularly, lncRNA GABPB1-AS1 was firstly proposed in AML. We believe that GABPB1-AS1 is expected to become a candidate prognostic biomarker or a potential therapeutic target.

Inferring Latent Disease-lncRNA Associations by Label-Propagation Algorithm and Random Projection on a Heterogeneous Network

Long noncoding RNA (lncRNA), a type of more than 200 nucleotides non-coding RNA, is related to various complex diseases. To precisely identify the potential lncRNA–disease association is important to understand the disease pathogenesis, to develop new drugs, and to design individualized diagnosis and treatment methods for different human diseases. Compared with the complexity and high cost of biological experiments, computational methods can quickly and effectively predict potential lncRNA–disease associations. Thus, it is a promising avenue to develop computational methods for lncRNA-disease prediction. However, owing to the low prediction accuracy ofstate of the art methods, it is vastly challenging to accurately and effectively identify lncRNA-disease at present. This article proposed an integrated method called LPARP, which is based on label-propagation algorithm and random projection to address the issue. Specifically, the label-propagation algorithm is initially used to obtain the estimated scores of lncRNA–disease associations, and then random projections are used to accurately predict disease-related lncRNAs.The empirical experiments showed that LAPRP achieved good prediction on three golddatasets, which is superior to existing state-of-the-art prediction methods. It can also be used to predict isolated diseases and new lncRNAs. Case studies of bladder cancer, esophageal squamous-cell carcinoma, and colorectal cancer further prove the reliability of the method. The proposed LPARP algorithm can predict the potential lncRNA–disease interactions stably and effectively with fewer data. LPARP can be used as an effective and reliable tool for biomedical research.

Crosstalk Among circRNA/lncRNA, miRNA, and mRNA in Osteoarthritis

Osteoarthritis (OA) is a joint disease that is pervasive in life, and the incidence and mortality of OA are increasing, causing many adverse effects on people's life. Therefore, it is very vital to identify new biomarkers and therapeutic targets in the clinical diagnosis and treatment of OA. ncRNA is a nonprotein-coding RNA that does not translate into proteins but participates in protein translation. At the RNA level, it can perform biological functions. Many studies have found that miRNA, lncRNA, and circRNA are closely related to the course of OA and play important regulatory roles in transcription, post-transcription, and post-translation, which can be used as biological targets for the prevention, diagnosis, and treatment of OA. In this review, we summarized and described the various roles of different types of miRNA, lncRNA, and circRNA in OA, the roles of different lncRNA/circRNA-miRNA-mRNA axis in OA, and the possible prospects of these ncRNAs in clinical application.

gGATLDA: lncRNA-disease association prediction based on graph-level graph attention network

Background

Long non-coding RNAs (lncRNAs) are related to human diseases by regulating gene expression. Identifying lncRNA-disease associations (LDAs) will contribute to diagnose, treatment, and prognosis of diseases. However, the identification of LDAs by the biological experiments is time-consuming, costly and inefficient. Therefore, the development of efficient and high-accuracy computational methods for predicting LDAs is of great significance.

Results

In this paper, we propose a novel computational method (gGATLDA) to predict LDAs based on graph-level graph attention network. Firstly, we extract the enclosing subgraphs of each lncRNA-disease pair. Secondly, we construct the feature vectors by integrating lncRNA similarity and disease similarity as node attributes in subgraphs. Finally, we train a graph neural network (GNN) model by feeding the subgraphs and feature vectors to it, and use the trained GNN model to predict lncRNA-disease potential association scores. The experimental results show that our method can achieve higher area under the receiver operation characteristic curve (AUC), area under the precision recall curve (AUPR), accuracy and F1-Score than the state-of-the-art methods in five fold cross-validation. Case studies show that our method can effectively identify lncRNAs associated with breast cancer, gastric cancer, prostate cancer, and renal cancer.

Conclusion

The experimental results indicate that our method is a useful approach for predicting potential LDAs.

Predicting Pseudogene-miRNA Associations Based on Feature Fusion and Graph Auto-Encoder

Pseudogenes were originally regarded as non-functional components scattered in the genome during evolution. Recent studies have shown that pseudogenes can be transcribed into long non-coding RNA and play a key role at multiple functional levels in different physiological and pathological processes. microRNAs (miRNAs) are a type of non-coding RNA, which plays important regulatory roles in cells. Numerous studies have shown that pseudogenes and miRNAs have interactions and form a ceRNA network with mRNA to regulate biological processes and involve diseases. Exploring the associations of pseudogenes and miRNAs will facilitate the clinical diagnosis of some diseases. Here, we propose a prediction model PMGAE (Pseudogene–MiRNA association prediction based on the Graph Auto-Encoder), which incorporates feature fusion, graph auto-encoder (GAE), and eXtreme Gradient Boosting (XGBoost). First, we calculated three types of similarities including Jaccard similarity, cosine similarity, and Pearson similarity between nodes based on the biological characteristics of pseudogenes and miRNAs. Subsequently, we fused the above similarities to construct a similarity profile as the initial representation features for nodes. Then, we aggregated the similarity profiles and associations of nodes to obtain the low-dimensional representation vector of nodes through a GAE. In the last step, we fed these representation vectors into an XGBoost classifier to predict new pseudogene–miRNA associations (PMAs). The results of five-fold cross validation show that PMGAE achieves a mean AUC of 0.8634 and mean AUPR of 0.8966. Case studies further substantiated the reliability of PMGAE for mining PMAs and the study of endogenous RNA networks in relation to diseases.

Fusion of KATZ measure and space projection to fast probe potential lncRNA-disease associations in bipartite graphs

It is well known that numerous long noncoding RNAs (lncRNAs) closely relate to the physiological and pathological processes of human diseases and can serves as potential biomarkers. Therefore, lncRNA-disease associations that are identified by computational methods as the targeted candidates reduce the cost of biological experiments focusing on deep study furtherly. However, inaccurate construction of similarity networks and inadequate numbers of observed known lncRNA–disease associations, such inherent problems make many mature computational methods that have been developed for many years still exit some limitations. It motivates us to explore a new computational method that was fused with KATZ measure and space projection to fast probing potential lncRNA-disease associations (namely KATZSP). KATZSP is comprised of following key steps: combining all the global information with which to change Boolean network of known lncRNA–disease associations into the weighted networks; changing the similarities calculation into counting the number of walks that connect lncRNA nodes and disease nodes in bipartite graphs; obtaining the space projection scores to refine the primary prediction scores. The process to fuse KATZ measure and space projection was simplified and uncomplicated with needing only one attenuation factor. The leave-one-out cross validation (LOOCV) experimental results showed that, compared with other state-of-the-art methods (NCPLDA, LDAI-ISPS and IIRWR), KATZSP had a higher predictive accuracy shown with area-under-the-curve (AUC) value on the three datasets built, while KATZSP well worked on inferring potential associations related to new lncRNAs (or isolated diseases). The results from real cases study (such as pancreas cancer, lung cancer and colorectal cancer) further confirmed that KATZSP is capable of superior predictive ability to be applied as a guide for traditional biological experiments.

Graph Triple-Attention Network for Disease-related LncRNA Prediction

Abnormal expressions of long non-coding RNAs (lncRNAs) are associated with various human diseases. Identifying disease-related lncRNAs can help clarify complex disease pathogeneses. The latest methods for lncRNA-disease association prediction rely on diverse data about lncRNAs and diseases. These methods, however, cannot adequately integrate the neighbour topological information of lncRNA and disease nodes. Moreover, more intrinsic features of lncRNA-disease node pairs can be explored to better predict the latent associations between lncRNAs and diseases. We developed a novel method, named GTAN, to predict the association propensities between lncRNAs and diseases. GTAN integrates various information about lncRNAs and diseases, including similarities, associations and interactions among lncRNAs, diseases and miRNAs, and exploits neighbour topology and attribute representations of a pair of lncRNA-disease nodes. We adopted in GTAN a graph neural network architecture with three attention mechanisms and multi-layer convolutional neural networks. First, a neighbour-level self-attention mechanism is constructed to learn the importance of each neighbour for an interested lncRNA or disease node. Second, topology-level attention is proposed to enhance contextual dependencies among multiple local topology representations of the lncRNA or disease node. An attention-enhanced graph neural network framework is then established to learn a topology representation of top-ranked neighbours for a pair of lncRNA-disease nodes. GTAN also has attribute-level attention to distinguish various contributions of attributes of the lncRNA-disease pair. Finally, attribute representation is learned by multi-layer CNN to integrate detailed features and representative features of the pair. Extensive experimental results demonstrated that GTAN outperformed state-of-the-art methods. The improved recall rates also showed GTANs capacity for retrieving more actual lncRNA-disease associations in the top-ranked candidates. The ablation studies confirmed the important contributions of three attention mechanisms. Case studies on lung cancer, prostate cancer and colon cancer further showed GTANs ability in discovering potential lncRNA candidates related to diseases.

Matrix factorization-based data fusion for the prediction of RNA-binding proteins and alternative splicing event associations during epithelial-mesenchymal transition

MOTIVATION

The epithelial-mesenchymal transition (EMT) is a cellular-developmental process activated during tumor metastasis. Transcriptional regulatory networks controlling EMT are well studied; however, alternative RNA splicing also plays a critical regulatory role during this process. Unfortunately, a comprehensive understanding of alternative splicing (AS) and the RNA-binding proteins (RBPs) that regulate it during EMT remains largely unknown. Therefore, a great need exists to develop effective computational methods for predicting associations of RBPs and AS events. Dramatically increasing data sources that have direct and indirect information associated with RBPs and AS events have provided an ideal platform for inferring these associations.

RESULTS

In this study, we propose a novel method for RBP-AS target prediction based on weighted data fusion with sparse matrix tri-factorization (WDFSMF in short) that simultaneously decomposes heterogeneous data source matrices into low-rank matrices to reveal hidden associations. WDFSMF can select and integrate data sources by assigning different weights to those sources, and these weights can be assigned automatically. In addition, WDFSMF can identify significant RBP complexes regulating AS events and eliminate noise and outliers from the data. Our proposed method achieves an area under the receiver operating characteristic curve (AUC) of $90.78\%$, which shows that WDFSMF can effectively predict RBP-AS event associations with higher accuracy compared with previous methods. Furthermore, this study identifies significant RBPs as complexes for AS events during EMT and provides solid ground for further investigation into RNA regulation during EMT and metastasis. WDFSMF is a general data fusion framework, and as such it can also be adapted to predict associations between other biological entities.

Circular RNAs and complex diseases: from experimental results to computational models

Circular RNAs (circRNAs) are a class of single-stranded, covalently closed RNA molecules with a variety of biological functions. Studies have shown that circRNAs are involved in a variety of biological processes and play an important role in the development of various complex diseases, so the identification of circRNA-disease associations would contribute to the diagnosis and treatment of diseases. In this review, we summarize the discovery, classifications and functions of circRNAs and introduce four important diseases associated with circRNAs. Then, we list some significant and publicly accessible databases containing comprehensive annotation resources of circRNAs and experimentally validated circRNA-disease associations. Next, we introduce some state-of-the-art computational models for predicting novel circRNA-disease associations and divide them into two categories, namely network algorithm-based and machine learning-based models. Subsequently, several evaluation methods of prediction performance of these computational models are summarized. Finally, we analyze the advantages and disadvantages of different types of computational models and provide some suggestions to promote the development of circRNA-disease association identification from the perspective of the construction of new computational models and the accumulation of circRNA-related data.

Predicting LncRNA-Disease Association by a Random Walk With Restart on Multiplex and Heterogeneous Networks

Studies have found that long non-coding RNAs (lncRNAs) play important roles in many human biological processes, and it is critical to explore potential lncRNA–disease associations, especially cancer-associated lncRNAs. However, traditional biological experiments are costly and time-consuming, so it is of great significance to develop effective computational models. We developed a random walk algorithm with restart on multiplex and heterogeneous networks of lncRNAs and diseases to predict lncRNA–disease associations (MHRWRLDA). First, multiple disease similarity networks are constructed by using different approaches to calculate similarity scores between diseases, and multiple lncRNA similarity networks are also constructed by using different approaches to calculate similarity scores between lncRNAs. Then, a multiplex and heterogeneous network was constructed by integrating multiple disease similarity networks and multiple lncRNA similarity networks with the lncRNA–disease associations, and a random walk with restart on the multiplex and heterogeneous network was performed to predict lncRNA–disease associations. The results of Leave-One-Out cross-validation (LOOCV) showed that the value of Area under the curve (AUC) was 0.68736, which was improved compared with the classical algorithm in recent years. Finally, we confirmed a few novel predicted lncRNAs associated with specific diseases like colon cancer by literature mining. In summary, MHRWRLDA contributes to predict lncRNA–disease associations.

GCRFLDA: scoring lncRNA-disease associations using graph convolution matrix completion with conditional random field

Long noncoding RNAs (lncRNAs) play important roles in various biological regulatory processes, and are closely related to the occurrence and development of diseases. Identifying lncRNA-disease associations is valuable for revealing the molecular mechanism of diseases and exploring treatment strategies. Thus, it is necessary to computationally predict lncRNA-disease associations as a complementary method for biological experiments. In this study, we proposed a novel prediction method GCRFLDA based on the graph convolutional matrix completion. GCRFLDA first constructed a graph using the available lncRNA-disease association information. Then, it constructed an encoder consisting of conditional random field and attention mechanism to learn efficient embeddings of nodes, and a decoder layer to score lncRNA-disease associations. In GCRFLDA, the Gaussian interaction profile kernels similarity and cosine similarity were fused as side information of lncRNA and disease nodes. Experimental results on four benchmark datasets show that GCRFLDA is superior to other existing methods. Moreover, we conducted case studies on four diseases and observed that 70 of 80 predicted associated lncRNAs were confirmed by the literature.

Prediction of Potential MicroRNA-Disease Association Using Kernelized Bayesian Matrix Factorization

MicroRNA (miRNA) molecules, which are effective in the formation and progression of many different diseases, are 18-22 nucleotides in length and make up a type of non-coding RNA. Predicting disease-related microRNAs is crucial for understanding the pathogenesis of disease and for diagnosis, treatment, and prevention of diseases. Many computational techniques have been studied and developed, as the experimental techniques used to find novel miRNA-disease associations in biology are costly. In this paper, a Kernelized Bayesian Matrix Factorization (KBMF) technique was suggested to predict new relations among miRNAs and diseases with several information such as miRNA functional similarity, disease semantic similarity, and known relations among miRNAs and diseases. AUC value of 0.9450 was obtained by implementing fivefold cross-validation for KBMF technique. We also carried out three kinds of case studies (breast, lung, and colon neoplasms) to prove the performance of KBMF technique, and the predictive reliability of this method was confirmed by the results. Thus, KBMF technique can be used as a reliable computational model to infer possible miRNA-disease associations.

Using Network Distance Analysis to Predict lncRNA-miRNA Interactions

LncRNA-miRNA interactions contribute to the regulation of therapeutic targets and diagnostic biomarkers in multifarious human diseases. However, it remains difficult to experimentally identify lncRNA-miRNA associations at large scale, and computational prediction methods are limited. In this study, we developed a network distance analysis model for lncRNA-miRNA association prediction (NDALMA). Similarity networks for lncRNAs and miRNAs were calculated and integrated with Gaussian interaction profile (GIP) kernel similarity. Then, network distance analysis was applied to the integrated similarity networks, and final scores were obtained after confidence calculation and score conversion. Our model obtained satisfactory results in fivefold cross validation, achieving an AUC of 0.8810 and an AUPR of 0.8315. Moreover, NDALMA showed superior prediction performance over several other network algorithms, and we tested the suitability and flexibility of the model by comparing different types of similarity. In addition, case studies of the relationships between lncRNAs and miRNAs were conducted, which verified the reliability of our method in predicting lncRNA-miRNA associations. The datasets and source code used in this study are available at https://github.com/Liu-Lab-Lnu/NDALMA .

Long Non-coding RNAs: Mechanisms, Experimental, and Computational Approaches in Identification, Characterization, and Their Biomarker Potential in Cancer

Long non-coding RNAs are diverse class of non-coding RNA molecules >200 base pairs of length having various functions like gene regulation, dosage compensation, epigenetic regulation. Dysregulation and genomic variations of several lncRNAs have been implicated in several diseases. Their tissue and developmental specific expression are contributing factors for them to be viable indicators of physiological states of the cells. Here we present an comprehensive review the molecular mechanisms and functions, state of the art experimental and computational pipelines and challenges involved in the identification and functional annotation of lncRNAs and their prospects as biomarkers. We also illustrate the application of co-expression networks on the TCGA-LIHC dataset for putative functional predictions of lncRNAs having a therapeutic potential in Hepatocellular carcinoma (HCC).

LDAH2V: Exploring Meta-Paths Across Multiple Networks for lncRNA-Disease Association Prediction

Accumulating evidence has demonstrated dysfunctions of long non-coding RNAs (lncRNAs) are involved in various complex human diseases. However, even today, the relationships between lncRNAs and diseases remain unknown in most cases. Developing effective computational approaches to identify potential lncRNA-disease associations has become a hot topic. Existing network-based approaches are usually focused on the intrinsic features of lncRNAs and diseases but ignore the heterogeneous information of biological networks. Considering the limitations in previous methods, we propose LDAH2V, an efficient computational framework for predicting potential lncRNA-disease associations. LDAH2V uses the HIN2Vec to calculate the meta-path and feature vector for each lncRNA-disease pair in the heterogeneous information network (HIN), which consists of lncRNA similarity network, disease similarity network, miRNA similarity network, and the associations between them. Then, a Gradient Boosting Tree (GBT) classifier to predict lncRNA-disease associations is built with the feature vectors. The results show that LDAH2V performs significantly better than the four existing state-of-the-art methods and gains an AUC of 0.97 in the 10-fold cross-validation test. Furthermore, case studies of colon cancer and ovarian cancer-related lncRNAs have been confirmed in related databases and medical literature.

BiGAN: LncRNA-disease association prediction based on bidirectional generative adversarial network

Background

An increasing number of studies have shown that lncRNAs are crucial for the control of hormones and the regulation of various physiological processes in the human body, and deletion mutations in RNA are related to many human diseases. LncRNA- disease association prediction is very useful for understanding pathogenesis, diagnosis, and prevention of diseases, and is helpful for labelling relevant biological information.

Results

In this manuscript, we propose a computational model named bidirectional generative adversarial network (BiGAN), which consists of an encoder, a generator, and a discriminator to predict new lncRNA-disease associations. We construct features between lncRNA and disease pairs by utilizing the disease semantic similarity, lncRNA sequence similarity, and Gaussian interaction profile kernel similarities of lncRNAs and diseases. The BiGAN maps the latent features of similarity features to predict unverified association between lncRNAs and diseases. The computational results have proved that the BiGAN performs significantly better than other state-of-the-art approaches in cross-validation. We employed the proposed model to predict candidate lncRNAs for renal cancer and colon cancer. The results are promising. Case studies show that almost 70% of lncRNAs in the top 10 prediction lists are verified by recent biological research.

Conclusion

The experimental results indicated that our proposed model had an accurate predictive ability for the association of lncRNA-disease pairs.

Identification of lncRNA Signature Associated With Pan-Cancer Prognosis

Long noncoding RNAs (lncRNAs) have emerged as potential prognostic markers in various human cancers as they participate in many malignant behaviors. However, the value of lncRNAs as prognostic markers among diverse human cancers is still under investigation, and a systematic signature based on these transcripts that related to pan-cancer prognosis has yet to be reported. In this study, we proposed a framework to incorporate statistical power, biological rationale, and machine learning models for pan-cancer prognosis analysis. The framework identified a 5-lncRNA signature (ENSG00000206567, PCAT29, ENSG00000257989, LOC388282, and LINC00339) from TCGA training studies (n = 1,878). The identified lncRNAs are significantly associated (all P ≤ 1.48E-11) with overall survival (OS) of the TCGA cohort (n = 4,231). The signature stratified the cohort into low- and high-risk groups with significantly distinct survival outcomes (median OS of 9.84 years versus 4.37 years, log-rank P = 1.48E-38) and achieved a time-dependent ROC/AUC of 0.66 at 5 years. After routine clinical factors involved, the signature demonstrated better performance for long-term prognostic estimation (AUC of 0.72). Moreover, the signature was further evaluated on two independent external cohorts (TARGET, n = 1,122; CPTAC, n = 391; National Cancer Institute) which yielded similar prognostic values (AUC of 0.60 and 0.75; log-rank P = 8.6E-09 and P = 2.7E-06). An indexing system was developed to map the 5-lncRNA signature to prognoses of pan-cancer patients. In silico functional analysis indicated that the lncRNAs are associated with common biological processes driving human cancers. The five lncRNAs, especially ENSG00000206567, ENSG00000257989 and LOC388282 that never reported before, may serve as viable molecular targets common among diverse cancers.

DSCMF: prediction of LncRNA-disease associations based on dual sparse collaborative matrix factorization

BACKGROUND

In the development of science and technology, there are increasing evidences that there are some associations between lncRNAs and human diseases. Therefore, finding these associations between them will have a huge impact on our treatment and prevention of some diseases. However, the process of finding the associations between them is very difficult and requires a lot of time and effort. Therefore, it is particularly important to find some good methods for predicting lncRNA-disease associations (LDAs).

RESULTS

In this paper, we propose a method based on dual sparse collaborative matrix factorization (DSCMF) to predict LDAs. The DSCMF method is improved on the traditional collaborative matrix factorization method. To increase the sparsity, the L2,1-norm is added in our method. At the same time, Gaussian interaction profile kernel is added to our method, which increase the network similarity between lncRNA and disease. Finally, the AUC value obtained by the experiment is used to evaluate the quality of our method, and the AUC value is obtained by the ten-fold cross-validation method.

CONCLUSIONS

The AUC value obtained by the DSCMF method is 0.8523. At the end of the paper, simulation experiment is carried out, and the experimental results of prostate cancer, breast cancer, ovarian cancer and colorectal cancer are analyzed in detail. The DSCMF method is expected to bring some help to lncRNA-disease associations research. The code can access the https://github.com/Ming-0113/DSCMF website.

Predicting LncRNA-Disease Association Based on Generative Adversarial Network

BACKGROUND

Increasing research reveals that long non-coding RNAs (lncRNAs) play an important role in various biological processes of human diseases. Nonetheless, only a handful of lncRNA-disease associations have been experimentally verified. The study of lncRNA-disease association prediction based on the computational model has provided a preliminary basis for biological experiments to a great degree so as to cut down the huge cost of wet lab experiments.

OBJECTIVE

This study aims to learn the real distribution of lncRNA-disease association from a limited number of known lncRNA-disease association data. This paper proposes a new lncRNA-disease association prediction model called LDA-GAN based on a generative adversarial network (GAN).

METHOD

Aiming at the problems of slow convergence rate, training instabilities, and unavailability of discrete data in traditional GAN, LDA-GAN utilizes the Gumbel-softmax technology to construct a differentiable process for simulating discrete sampling. Meanwhile, the generator and the discriminator of LDA-GAN are integrated to establish the overall optimization goal based on the pairwise loss function.

RESULTS

Experiments on standard datasets demonstrate that LDA-GAN achieves not only high stability and high efficiency in the process of confrontation learning but also gives full play to the semi-supervised learning advantage of generative adversarial learning framework for unlabeled data, which further improves the prediction accuracy of lncRNA-disease association. Besides, case studies show that LDA-GAN can accurately generate potential diseases for several lncRNAs.

RWSF-BLP: a novel lncRNA-disease association prediction model using random walk-based multi-similarity fusion and bidirectional label propagation

An increasing number of studies and experiments have demonstrated that long noncoding RNAs (lncRNAs) have a massive impact on various biological processes. Predicting potential associations between lncRNAs and diseases not only can improve our understanding of the molecular mechanisms of human diseases but also can facilitate the identification of biomarkers for disease diagnosis, treatment, and prevention. However, identifying such associations through experiments is costly and demanding, thereby prompting researchers to develop computational methods to complement these experiments. In this paper, we constructed a novel model called RWSF-BLP (a novel lncRNA-disease association prediction model using Random Walk-based multi-Similarity Fusion and Bidirectional Label Propagation), which applies an efficient random walk-based multi-similarity fusion (RWSF) method to fuse different similarity matrices and utilizes bidirectional label propagation to predict potential lncRNA-disease associations. Leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold-CV) were implemented in the evaluation RWSF-BLP performance. Results showed that, RWSF-BLP has reliable AUCs of 0.9086 and 0.9115 ± 0.0044 under the framework of LOOCV and 5-fold-CV and outperformed other four canonical methods. Case studies on lung cancer and leukemia demonstrated that potential lncRNA-disease associations can be predicted through our method. Therefore, our method can accurately infer potential lncRNA-disease associations and may be a good choice in future biomedical research.

Predicting lncRNA-miRNA interactions based on interactome network and graphlet interaction

In the development and treatment of many human diseases, the regulatory roles between lncRNAs and miRNAs are important, but much remains unknown about them; moreover, experimental methods for analyzing them are expensive and time-consuming. In this work, we applied a semi-supervised interactome network-based approach to explore and forecast the latent interaction between lncRNAs and miRNAs. We constructed graphs according to the similarity of each of lncRNAs and miRNAs and determined the number of graphlet interaction isomers between nodes in these two graphs. According to the two graphs and the known interactive relationship, we calculated a score for lncRNA-miRNA pairs, as the prediction result. The results showed that the model (LMI-INGI) was reliable in fivefold cross-validation (AUC = 0.8957, PRE = 0.6815, REC = 0.8842, F1 score = 0.7452, AUPR = 0.9213). We also tested the model with data based on the similarity of expression profile and similarity of function for verifying the applicability of LMI-INGI, and the resulting AUC value was 0.9197 and 0.9006, respectively. Compared with the other four algorithms and variable similarity tests, our model successfully demonstrated superiority and good generalizability. LMI-INGI would be helpful in forecasting interactions between lncRNAs and miRNAs.

Prediction of RNA-binding protein and alternative splicing event associations during epithelial-mesenchymal transition based on inductive matrix completion

MOTIVATION

The developmental process of epithelial-mesenchymal transition (EMT) is abnormally activated during breast cancer metastasis. Transcriptional regulatory networks that control EMT have been well studied; however, alternative RNA splicing plays a vital regulatory role during this process and the regulating mechanism needs further exploration. Because of the huge cost and complexity of biological experiments, the underlying mechanisms of alternative splicing (AS) and associated RNA-binding proteins (RBPs) that regulate the EMT process remain largely unknown. Thus, there is an urgent need to develop computational methods for predicting potential RBP-AS event associations during EMT.

RESULTS

We developed a novel model for RBP-AS target prediction during EMT that is based on inductive matrix completion (RAIMC). Integrated RBP similarities were calculated based on RBP regulating similarity, and RBP Gaussian interaction profile (GIP) kernel similarity, while integrated AS event similarities were computed based on AS event module similarity and AS event GIP kernel similarity. Our primary objective was to complete missing or unknown RBP-AS event associations based on known associations and on integrated RBP and AS event similarities. In this paper, we identify significant RBPs for AS events during EMT and discuss potential regulating mechanisms. Our computational results confirm the effectiveness and superiority of our model over other state-of-the-art methods. Our RAIMC model achieved AUC values of 0.9587 and 0.9765 based on leave-one-out cross-validation (CV) and 5-fold CV, respectively, which are larger than the AUC values from the previous models. RAIMC is a general matrix completion framework that can be adopted to predict associations between other biological entities. We further validated the prediction performance of RAIMC on the genes CD44 and MAP3K7. RAIMC can identify the related regulating RBPs for isoforms of these two genes.

AVAILABILITY AND IMPLEMENTATION

The source code for RAIMC is available at https://github.com/yushanqiu/RAIMC.

CONTACT

zouquan@nclab.net online.

GAERF: predicting lncRNA-disease associations by graph auto-encoder and random forest

Predicting disease-related long non-coding RNAs (lncRNAs) is beneficial to finding of new biomarkers for prevention, diagnosis and treatment of complex human diseases. In this paper, we proposed a machine learning techniques-based classification approach to identify disease-related lncRNAs by graph auto-encoder (GAE) and random forest (RF) (GAERF). First, we combined the relationship of lncRNA, miRNA and disease into a heterogeneous network. Then, low-dimensional representation vectors of nodes were learned from the network by GAE, which reduce the dimension and heterogeneity of biological data. Taking these feature vectors as input, we trained a RF classifier to predict new lncRNA-disease associations (LDAs). Related experiment results show that the proposed method for the representation of lncRNA-disease characterizes them accurately. GAERF achieves superior performance owing to the ensemble learning method, outperforming other methods significantly. Moreover, case studies further demonstrated that GAERF is an effective method to predict LDAs.

WGRCMF: A Weighted Graph Regularized Collaborative Matrix Factorization Method for Predicting Novel LncRNA-Disease Associations

In recent years, many human diseases have been determined to be associated with certain lncRNAs. Only a small percentage of all lncRNA-disease associations (LDAs) have been discovered by researchers. Predicting novel LDAs is time-consuming and costly. It is crucial to propose a method that can effectively identify potential LDAs to solve this problem based on the available datasets. Although some current methods can effectively predict potential LDAs, the prediction accuracy needs to be improved, and there are few known associations. Moreover, there are notable errors in the method of constructing the network and the bipartite graph, which interfere with the final results. A weighted graph regularized collaborative matrix factorization (WGRCMF) method is proposed to predict novel LDAs. We introduce the graph regularization terms into the collaborative matrix factorization. Considering that manifold learning can recover low-dimensional manifold structures from high-dimensional sampled data, we can find low-dimensional manifolds in high-dimensional space. In addition, a weight matrix is also introduced into the method, the significance of which is to prevent unknown associations from contributing to the final prediction matrix. Finally, the prediction accuracy of this method is better than those of other methods. In several cancer cases, we implemented the corresponding simulation experiments. According to the experimental results, the proposed method is feasible and effective.

HAUBRW: Hybrid algorithm and unbalanced bi-random walk for predicting lncRNA-disease associations

An increasing number of research shows that long non-coding RNA plays a key role in many important biological processes. However, the number of disease-related lncRNAs found by researchers remains relatively small, and experimental identification is time consuming and labor intensive. In this study, we propose a novel method, namely HAUBRW, to predict undiscovered lncRNA-disease associations. First, the hybrid algorithm, which combines the heat spread algorithm and the probability diffusion algorithm, redistributes the resources. Second, unbalanced bi-random walk, is used to infer undiscovered lncRNA disease associations. Seven advanced models, i.e. BRWLDA, DSCMF, RWRlncD, IDLDA, KATZ, Ping's, and Yang's were compared with our method, and simulation results show that the AUC of our method is more perfect than the other models. In addition, case studies have shown that HAUBRW can effectively predict candidate lncRNAs for breast, osteosarcoma and cervical cancer. Therefore, our approach may be a good choice in future biomedical research.