A Novel Method for LncRNA-Disease Association Prediction Based on an lncRNA-Disease Association Network

Mask-Guided Target Node Feature Learning and Dynamic Detailed Feature Enhancement for lncRNA-Disease Association Prediction

Identifying new relevant long noncoding RNAs (lncRNAs) for various human diseases can facilitate the exploration of the causes and progression of these diseases. Recently, several graph inference methods have been proposed to predict disease-related lncRNAs by exploiting the topological structure and node attributes within graphs. However, these methods did not prioritize the target lncRNA and disease nodes over auxiliary nodes like miRNA nodes, potentially limiting their ability to fully utilize the features of the target nodes. We propose a new method, mask-guided target node feature learning and dynamic detailed feature enhancement for lncRNA-disease association prediction (MDLD), to enhance node feature learning for improved lncRNA-disease association prediction. First, we designed a heterogeneous graph masked transformer autoencoder to guide feature learning, focusing more on the features of target lncRNA (disease) nodes. The target nodes were increasingly masked as training progressed, which helps develop a more robust prediction model. Second, we developed a graph convolutional network with dynamic residuals (GCNDR) to learn and integrate the heterogeneous topology and features of all lncRNA, disease, and miRNA nodes. GCNDR employs an interlayer residual strategy and a residual evolution strategy to mitigate oversmoothing caused by multilayer graph convolution. The interlayer residual strategy estimates the importance of node features learned in the previous GCN encoding layer for nodes in the current encoding layer. Additionally, since there are dependencies in the importance of features of individual lncRNA (disease, miRNA) nodes across multiple encoding layers, a gated recurrent unit-based strategy is proposed to encode these dependencies. Finally, we designed a perspective-level attention mechanism to obtain more informative features of lncRNA and disease node pairs from the perspectives of mask-enhanced and dynamic-enhanced node features. Cross-validation experimental results demonstrated that MDLD outperformed 10 other state-of-the-art prediction methods. Ablation experiments and case studies on candidate lncRNAs for three diseases further proved the technical contributions of MDLD and its capability to discover disease-related lncRNAs.

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.

SAGESDA: Multi-GraphSAGE networks for predicting SnoRNA-disease associations

Over the years, extensive research has highlighted the functional roles of small nucleolar RNAs in various biological processes associated with the development of complex human diseases. Therefore, understanding the existing relationships between different snoRNAs and diseases is crucial for advancing disease diagnosis and treatment. However, classical biological experiments for identifying snoRNA-disease associations are expensive and time-consuming. Therefore, there is an urgent need for cost-effective computational techniques that can enhance the efficiency and accuracy of prediction. While several computational models have already been proposed, many suffer from limitations and suboptimal performance. In this study, we introduced a novel Graph Neural Network-based (GNN) classification model, called SAGESDA, which is implemented through the GraphSAGE architecture with attention for the prediction of snoRNA-disease associations. The classifier leverages local neighbouring nodes in a heterogeneous network to generate new node embeddings through message passing. The mini-batch gradient descent technique was applied to divide the graph into smaller sub-graphs, which enhances the model's accuracy, speed and scalability. With these advancements, SAGESDA attained an area under the receiver operating characteristic (ROC) curve (AUC) of 0.92 using the standard dot product classifier, surpassing previous related studies. This notable performance demonstrates that SAGESDA is a promising model for predicting unknown snoRNA-disease associations with high accuracy. The SAGESDA implementation details can be obtained from https://github.com/momanyibiffon/SAGESDA.git.

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.

Specific topology and topological connection sensitivity enhanced graph learning for lncRNA-disease association prediction

Predicting disease-related candidate long noncoding RNAs (lncRNAs) is beneficial for exploring disease pathogenesis due to the close relations between lncRNAs and the occurrence and development of human diseases. It is a long-term and challenging task to adequately extract specific and local topologies in individual lncRNA network and individual disease network, and integrate the information of the connection relationships. We propose a new graph learning-based prediction method to encode specific and local topologies from each individual network, neighbor topologies with different connection relationships, and pairwise attributes. We first construct a lncRNA network composed of all the lncRNA nodes and their similarities, and a single disease network that contains all the disease nodes and disease similarities. Then, a network-aware graph convolutional autoencoder is constructed to encode the specific and local topologies of each network. Secondly, a heterogeneous network is established to embed all lncRNA, disease, and miRNA nodes and their various connections. Afterwards, a connection-sensitive graph neural network is designed to deeply integrate the neighbor node attributes and connection characteristics in the heterogeneous network and learn neighbor topological representations. We also construct both connection-level and topology representation-level attention mechanisms to extract informative connections and topological representations. Finally, we build a multi-layer convolutional neural networks with weighted residuals to adaptively complement the detailed features to pairwise attribute encoding. Comprehensive experiments and comparison results demonstrated that NCPred outperforms seven state-of-the-art prediction methods. The ablation studies demonstrated the importance of local topology learning, neighbor topology learning, and pairwise attribute encoding. Case studies on prostate, lung, and breast cancers further revealed NCPred's capacity to screen potential candidate disease-related lncRNAs.

Association filtering and generative adversarial networks for predicting lncRNA-associated disease

BACKGROUND

Long non-coding RNA (lncRNA) closely associates with numerous biological processes, and with many diseases. Therefore, lncRNA-disease association prediction helps obtain relevant biological information and understand pathogenesis, and thus better diagnose preventable diseases.

RESULTS

Herein, we offer the LDAF_GAN method for predicting lncRNA-associated disease based on association filtering and generative adversarial networks. Experimentation used two types of data: lncRNA-disease associated data without lncRNA sequence features, and fused lncRNA sequence features. LDAF_GAN uses a generator and discriminator, and differs from the original GAN by the addition of a filtering operation and negative sampling. Filtering allows the generator output to filter out unassociated diseases before being fed into the discriminator. Thus, the results generated by the model focuses only on lncRNAs associated with disease. Negative sampling takes a portion of disease terms with 0 from the association matrix as negative samples, which are assumed to be unassociated with lncRNA. A regular term is added to the loss function to avoid producing a vector with all values of 1, which can fool the discriminator. Thus, the model requires that generated positive samples are close to 1, and negative samples are close to 0. The model achieved a superior fitting effect; LDAF_GAN had superior performance in predicting fivefold cross-validations on the two datasets with AUC values of 0.9265 and 0.9278, respectively. In the case study, LDAF_GAN predicted disease association for six lncRNAs-H19, MALAT1, XIST, ZFAS1, UCA1, and ZEB1-AS1-and with the top ten predictions of 100%, 80%, 90%, 90%, 100%, and 90%, respectively, which were reported by previous studies.

CONCLUSION

LDAF_GAN efficiently predicts the potential association of existing lncRNAs and the potential association of new lncRNAs with diseases. The results of fivefold cross-validation, tenfold cross-validation, and case studies suggest that the model has great predictive potential for lncRNA-disease association prediction.

A lncRNA-disease association prediction tool development based on bridge heterogeneous information network via graph representation learning for family medicine and primary care

Identification of long non-coding RNAs (lncRNAs) associated with common diseases is crucial for patient self-diagnosis and monitoring of health conditions using artificial intelligence (AI) technology at home. LncRNAs have gained significant attention due to their crucial roles in the pathogenesis of complex human diseases and identifying their associations with diseases can aid in developing diagnostic biomarkers at the molecular level. Computational methods for predicting lncRNA-disease associations (LDAs) have become necessary due to the time-consuming and labor-intensive nature of wet biological experiments in hospitals, enabling patients to access LDAs through their AI terminal devices at any time. Here, we have developed a predictive tool, LDAGRL, for identifying potential LDAs using a bridge heterogeneous information network (BHnet) constructed via Structural Deep Network Embedding (SDNE). The BHnet consists of three types of molecules as bridge nodes to implicitly link the lncRNA with disease nodes and the SDNE is used to learn high-quality node representations and make LDA predictions in a unified graph space. To assess the feasibility and performance of LDAGRL, extensive experiments, including 5-fold cross-validation, comparison with state-of-the-art methods, comparison on different classifiers and comparison of different node feature combinations, were conducted, and the results showed that LDAGRL achieved satisfactory prediction performance, indicating its potential as an effective LDAs prediction tool for family medicine and primary care.

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.

Data resources and computational methods for lncRNA-disease association prediction

Increasing interest has been attracted in deciphering the potential disease pathogenesis through lncRNA-disease association (LDA) prediction, regarding to the diverse functional roles of lncRNAs in genome regulation. Whilst, computational models and algorithms benefit systematic biology research, even facilitate the classical biological experimental procedures. In this review, we introduce representative diseases associated with lncRNAs, such as cancers, cardiovascular diseases, and neurological diseases. Current publicly available resources related to lncRNAs and diseases have also been included. Furthermore, all of the 64 computational methods for LDA prediction have been divided into 5 groups, including machine learning-based methods, network propagation-based methods, matrix factorization- and completion-based methods, deep learning-based methods, and graph neural network-based methods. The common evaluation methods and metrics in LDA prediction have also been discussed. Finally, the challenges and future trends in LDA prediction have been discussed. Recent advances in LDA prediction approaches have been summarized in the GitHub repository at https://github.com/sheng-n/lncRNA-disease-methods.

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.

LDAformer: predicting lncRNA-disease associations based on topological feature extraction and Transformer encoder

The identification of long noncoding RNA (lncRNA)-disease associations is of great value for disease diagnosis and treatment, and it is now commonly used to predict potential lncRNA-disease associations with computational methods. However, the existing methods do not sufficiently extract key features during data processing, and the learning model parts are either less powerful or overly complex. Therefore, there is still potential to achieve better predictive performance by improving these two aspects. In this work, we propose a novel lncRNA-disease association prediction method LDAformer based on topological feature extraction and Transformer encoder. We construct the heterogeneous network by integrating the associations between lncRNAs, diseases and micro RNAs (miRNAs). Intra-class similarities and inter-class associations are presented as the lncRNA-disease-miRNA weighted adjacency matrix to unify semantics. Next, we design a topological feature extraction process to further obtain multi-hop topological pathway features latent in the adjacency matrix. Finally, to capture the interdependencies between heterogeneous pathways, a Transformer encoder based on the global self-attention mechanism is employed to predict lncRNA-disease associations. The efficient feature extraction and the intuitive and powerful learning model lead to ideal performance. The results of computational experiments on two datasets show that our method outperforms the state-of-the-art baseline methods. Additionally, case studies further indicate its capability to discover new associations accurately.

MSF-UBRW: An Improved Unbalanced Bi-Random Walk Method to Infer Human lncRNA-Disease Associations

Long-non-coding RNA (lncRNA) is a transcription product that exerts its biological functions through a variety of mechanisms. The occurrence and development of a series of human diseases are closely related to abnormal expression levels of lncRNAs. Scientists have developed many computational models to identify the lncRNA-disease associations (LDAs). However, many potential LDAs are still unknown. In this paper, a novel method, namely MSF-UBRW (multiple similarities fusion based on unbalanced bi-random walk), is designed to explore new LDAs. First, two similarities (functional similarity and Gaussian Interaction Profile kernel similarity) of lncRNAs are calculated and fused linearly, also for disease data. Then, the known association matrix is preprocessed. Next, the linear neighbor similarities of lncRNAs and diseases are calculated, respectively. After that, the potential associations are predicted based on unbalanced bi-random walk. The fusion of multiple similarities improves the prediction performance of MSF-UBRW to a large extent. Finally, the prediction ability of the MSF-UBRW algorithm is measured by two statistical methods, leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold CV). The AUCs of 0.9391 in LOOCV and 0.9183 (±0.0054) in 5-fold CV confirmed the reliable prediction ability of the MSF-UBRW method. Case studies of three common diseases also show that the MSF-UBRW method can infer new LDAs effectively.

Identification of Essential Proteins Based on Local Random Walk and Adaptive Multi-View Multi-Label Learning

Accumulating evidences have indicated that essential proteins play vital roles in human physiological process. In recent years, although researches on prediction of essential proteins have been developing rapidly, there are as well various limitations such as unsatisfactory data suitability, low accuracy of predictive results and so on. In this manuscript, a novel method called RWAMVL was proposed to predict essential proteins based on the Random Walk and the Adaptive Multi-View multi-label Learning. In RWAMVL, considering that the inherent noise is ubiquitous in existing datasets of known protein-protein interactions (PPIs), a variety of different features including biological features of proteins and topological features of PPI networks were obtained by adopting adaptive multi-view multi-label learning first. And then, an improved random walk method was designed to detect essential proteins based on these different features. Finally, in order to verify the predictive performance of RWAMVL, intensive experiments were done to compare it with multiple state-of-the-art predictive methods under different expeditionary frameworks. And as a result, RWAMVL was proven that it can achieve better prediction accuracy than all those competitive methods, which demonstrated as well that RWAMVL may be a potential tool for prediction of key proteins in the future.

DHNLDA: A Novel Deep Hierarchical Network Based Method for Predicting lncRNA-Disease Associations

Recent studies have found that lncRNA (long non-coding RNA) in ncRNA (non-coding RNA) is not only involved in many biological processes, but also abnormally expressed in many complex diseases. Identification of lncRNA-disease associations accurately is of great significance for understanding the function of lncRNA and disease mechanism. In this paper, a deep learning framework consisting of stacked autoencoder(SAE), multi-scale ResNet and stacked ensemble module, named DHNLDA, was constructed to predict lncRNA-disease associations, which integrates multiple biological data sources and constructing feature matrices. Among them, the biological data including the similarity and the interaction of lncRNAs, diseases and miRNAs are integrated. The feature matrices are obtained by node2vec embedding and feature extraction respectively. Then, the SAE and the multi-scale ResNet are used to learn the complementary information between nodes, and the high-level features of node attributes are obtained. Finally, the fusion of high-level feature is input into the stacked ensemble module to obtain the prediction results of lncRNA-disease associations. The experimental results of five-fold cross-validation show that the AUC of DHNLDA reaches 0.975 better than the existing methods. Case studies of stomach cancer, breast cancer and lung cancer have shown the great ability of DHNLDA to discover the potential lncRNA-disease associations.

BLNIMDA: identifying miRNA-disease associations based on weighted bi-level network

Background

MicroRNAs (miRNAs) have been confirmed to be inextricably linked to the emergence of human complex diseases. The identification of the disease-related miRNAs has gradually become a routine way to unveil the genetic mechanisms of examined disorders.

Methods

In this study, a method BLNIMDA based on a weighted bi-level network was proposed for predicting hidden associations between miRNAs and diseases. For this purpose, the known associations between miRNAs and diseases as well as integrated similarities between miRNAs and diseases are mapped into a bi-level network. Based on the developed bi-level network, the miRNA-disease associations (MDAs) are defined as strong associations, potential associations and no associations. Then, each miRNA-disease pair (MDP) is assigned two information properties according to the bidirectional information distribution strategy, i.e., associations of miRNA towards disease and vice-versa. Finally, two affinity weights for each MDP obtained from the information properties and the association type are then averaged as the final association score of the MDP. Highlights of the BLNIMDA lie in the definition of MDA types, and the introduction of affinity weights evaluation from the bidirectional information distribution strategy and defined association types, which ensure the comprehensiveness and accuracy of the final prediction score of MDAs.

Results

Five-fold cross-validation and leave-one-out cross-validation are used to evaluate the performance of the BLNIMDA. The results of the Area Under Curve show that the BLNIMDA has many advantages over the other seven selected computational methods. Furthermore, the case studies based on four common diseases and miRNAs prove that the BLNIMDA has good predictive performance.

Conclusions

Therefore, the BLNIMDA is an effective method for predicting hidden MDAs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08908-8.

Learning global dependencies and multi-semantics within heterogeneous graph for predicting disease-related lncRNAs

Motivation

Long noncoding RNAs (lncRNAs) play an important role in the occurrence and development of diseases. Predicting disease-related lncRNAs can help to understand the pathogenesis of diseases deeply. The existing methods mainly rely on multi-source data related to lncRNAs and diseases when predicting the associations between lncRNAs and diseases. There are interdependencies among node attributes in a heterogeneous graph composed of all lncRNAs, diseases and micro RNAs. The meta-paths composed of various connections between them also contain rich semantic information. However, the existing methods neglect to integrate attribute information of intermediate nodes in meta-paths.

Results

We propose a novel association prediction model, GSMV, to learn and deeply integrate the global dependencies, semantic information of meta-paths and node-pair multi-view features related to lncRNAs and diseases. We firstly formulate the global representations of the lncRNA and disease nodes by establishing a self-attention mechanism to capture and learn the global dependencies among node attributes. Second, starting from the lncRNA and disease nodes, respectively, multiple meta-pathways are established to reveal different semantic information. Considering that each meta-path contains specific semantics and has multiple meta-path instances which have different contributions to revealing meta-path semantics, we design a graph neural network based module which consists of a meta-path instance encoding strategy and two novel attention mechanisms. The proposed meta-path instance encoding strategy is used to learn the contextual connections between nodes within a meta-path instance. One of the two new attention mechanisms is at the meta-path instance level, which learns rich and informative meta-path instances. The other attention mechanism integrates various semantic information from multiple meta-paths to learn the semantic representation of lncRNA and disease nodes. Finally, a dilated convolution-based learning module with adjustable receptive fields is proposed to learn multi-view features of lncRNA-disease node pairs. The experimental results prove that our method outperforms seven state-of-the-art comparing methods for lncRNA-disease association prediction. Ablation experiments demonstrate the contributions of the proposed global representation learning, semantic information learning, pairwise multi-view feature learning and the meta-path instance encoding strategy. Case studies on three cancers further demonstrate our method’s ability to discover potential disease-related lncRNA candidates.

Contact

zhang@hlju.edu.cn or peiliangwu@ysu.edu.cn

Supplementary information

Supplementary data are available at Briefings in Bioinformatics online.

MCA-Net: Multi-Feature Coding and Attention Convolutional Neural Network for Predicting lncRNA-Disease Association

With the advent of the era of big data, it is troublesome to accurately predict the associations between lncRNAs and diseases based on traditional biological experiments due to its time-consuming and subjective. In this paper, we propose a novel deep learning method for predicting lncRNA-disease associations using multi-feature coding and attention convolutional neural network (MCA-Net). We first calculate six similarity features to extract different types of lncRNA and disease feature information. Second, a multi-feature coding method is proposed to construct the feature vectors of lncRNA-disease association samples by integrating the six similarity features. Furthermore, an attention convolutional neural network is developed to identify lncRNA-disease associations under 10-fold cross-validation. Finally, we evaluate the performance of MCA-Net from different perspectives including the effects of the model parameters, distinct deep learning models, and the necessity of attention mechanism. We also compare MCA-Net with several state-of-the-art methods on three publicly available datasets, i.e., LncRNADisease, Lnc2Cancer, and LncRNADisease2.0. The results show that our MCA-Net outperforms the state-of-the-art methods on all three dataset. Besides, case studies on breast cancer and lung cancer further verify that MCA-Net is effective and accurate for the lncRNA-disease association prediction.

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.

Graph Convolutional Auto-Encoders for Predicting Novel lncRNA-Disease Associations

LncRNAs are intermediate molecules that participate in the most diverse biological processes in humans, such as gene expression control and X-chromosome inactivation. Numerous researches have associated lncRNAs with a wide range of diseases, such as breast cancer, leukemia, and many other conditions. In this work, we propose a graph-based method named PANDA. This method treats the prediction of new associations between lncRNAs and diseases as a link prediction problem in a graph. We start by building a heterogeneous graph that contains the known associations between lncRNAs and diseases and additional information such as gene expression levels and symptoms of diseases. We then use a Graph Auto-encoder to learn the representation of the nodes' features and edges, finally applying a Neural Network to predict potentially interesting novel edges. The experimental results indicate that PANDA achieved a 0.976 AUC-ROC, surpassing state-of-the-art methods for the same problem, showing that PANDA could be a promising approach to generate embeddings to predict potentially novel lncRNA-disease associations.

Dual Attention Mechanisms and Feature Fusion Networks Based Method for Predicting LncRNA-Disease Associations

LncRNAs play a part in numerous momentous processes of biology such as disease diagnoses, preventions and treatments. The associations between various diseases and lncRNAs are one of the crucial approaches to learn the role and status of lncRNAs in human diseases. With the researches on lncRNA and diseases, multiple methods based on neural network have been employed to predict these associations. However, the deep and complicated characteristic representations of lncRNA-disease associations were failed to be extracted, and the discriminative contributions of the interactions, correlations, and similarities among miRNAs diseases, and lncRNAs for the correlation predictions were ignored. In this paper, based on the multibiology premise of lncRNAs, miRNAs, and diseases, a dual attention network was proposed to predict the model of lncRNA-disease associations for miRNAs, the disease characteristic matrix, and lncRNAs. Through two attention modules, we enable the model to learn the nonlinear, more complex and useful features of lncRNA, miRNA, and disease characteristic matrix. For the feature embedding matrix composed of lncRNA-disease, the connection between lncRNA-disease feature embedding matrix and lncRNA, miRNA, and disease characteristic matrix was enhanced through deconvolution and feature fusion layer. Compared with several latest methods, the method proposed in this paper can produce better performance. Researches on the cases of osteosarcoma, lung cancer, and gastric cancer have confirmed the effective recognition of potential lncRNA-disease associations.

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.

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.

Multi-channel graph attention autoencoders for disease-related lncRNAs prediction

MOTIVATION

Predicting disease-related long non-coding RNAs (lncRNAs) can be used as the biomarkers for disease diagnosis and treatment. The development of effective computational prediction approaches to predict lncRNA-disease associations (LDAs) can provide insights into the pathogenesis of complex human diseases and reduce experimental costs. However, few of the existing methods use microRNA (miRNA) information and consider the complex relationship between inter-graph and intra-graph in complex-graph for assisting prediction.

RESULTS

In this paper, the relationships between the same types of nodes and different types of nodes in complex-graph are introduced. We propose a multi-channel graph attention autoencoder model to predict LDAs, called MGATE. First, an lncRNA-miRNA-disease complex-graph is established based on the similarity and correlation among lncRNA, miRNA and diseases to integrate the complex association among them. Secondly, in order to fully extract the comprehensive information of the nodes, we use graph autoencoder networks to learn multiple representations from complex-graph, inter-graph and intra-graph. Thirdly, a graph-level attention mechanism integration module is adopted to adaptively merge the three representations, and a combined training strategy is performed to optimize the whole model to ensure the complementary and consistency among the multi-graph embedding representations. Finally, multiple classifiers are explored, and Random Forest is used to predict the association score between lncRNA and disease. Experimental results on the public dataset show that the area under receiver operating characteristic curve and area under precision-recall curve of MGATE are 0.964 and 0.413, respectively. MGATE performance significantly outperformed seven state-of-the-art methods. Furthermore, the case studies of three cancers further demonstrate the ability of MGATE to identify potential disease-correlated candidate lncRNAs. The source code and supplementary data are available at https://github.com/sheng-n/MGATE.

CONTACT

huanglan@jlu.edu.cn, wy6868@jlu.edu.cn.

Bioinformatics Analysis of Long Non-coding RNA and Related Diseases: An Overview

Long non-coding RNAs (lncRNAs) are usually located in the nucleus and cytoplasm of cells. The transcripts of lncRNAs are >200 nucleotides in length and do not encode proteins. Compared with small RNAs, lncRNAs have longer sequences, more complex spatial structures, and more diverse and complex mechanisms involved in the regulation of gene expression. LncRNAs are widely involved in the biological processes of cells, and in the occurrence and development of many human diseases. Many studies have shown that lncRNAs can induce the occurrence of diseases, and some lncRNAs undergo specific changes in tumor cells. Research into the roles of lncRNAs has covered the diagnosis of, for example, cardiovascular, cerebrovascular, and central nervous system diseases. The bioinformatics of lncRNAs has gradually become a research hotspot and has led to the discovery of a large number of lncRNAs and associated biological functions, and lncRNA databases and recognition models have been developed. In this review, the research progress of lncRNAs is discussed, and lncRNA-related databases and the mechanisms and modes of action of lncRNAs are described. In addition, disease-related lncRNA methods and the relationships between lncRNAs and human lung adenocarcinoma, rectal cancer, colon cancer, heart disease, and diabetes are discussed. Finally, the significance and existing problems of lncRNA research are considered.

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.

Identifying Microbe-Disease Association Based on a Novel Back-Propagation Neural Network Model

Over the years, numerous evidences have demonstrated that microbes living in the human body are closely related to human life activities and human diseases. However, traditional biological experiments are time-consuming and expensive, so it has become a research topic in bioinformatics to predict potential microbe-disease associations by adopting computational methods. In this study, a novel calculative method called BPNNHMDA is proposed to identify potential microbe-disease associations. In BPNNHMDA, a novel neural network model is first designed to infer potential microbe-disease associations, its input signal is a matrix of known microbe-disease associations, and its output signal is matrix of potential microbe-disease associations probabilities. And moreover, in the novel neural network model, a new activation function is designed to activate the hidden layer and the output layer based on the hyperbolic tangent function, and its initial connection weights are optimized by adopting Gaussian Interaction Profile kernel (GIP) similarity for microbes, which can improve the training speed of BPNNHMDA efficiently. Finally, in order to verify the performance of our prediction model, different frameworks such as the Leave-One-Out Cross Validation (LOOCV) and k-Fold Cross Validation ( k-Fold CV) are implemented on BPNNHMDA respectively. Simulation results illustrate that BPNNHMDA can achieve reliable AUCs of 0.9242, 0.9127 ± 0.0009 and 0.8955 ± 0.0018 in LOOCV, 5-Fold CV and 2-Fold CV separately, which are superior to previous state-of-the-art methods. Furthermore, case studies of inflammatory bowel disease (IBD), asthma and obesity demonstrate that BPNNHMDA has excellent prediction ability in practical applications as well.

An iteration model for identifying essential proteins by combining comprehensive PPI network with biological information

Background

Essential proteins have great impacts on cell survival and development, and played important roles in disease analysis and new drug design. However, since it is inefficient and costly to identify essential proteins by using biological experiments, then there is an urgent need for automated and accurate detection methods. In recent years, the recognition of essential proteins in protein interaction networks (PPI) has become a research hotspot, and many computational models for predicting essential proteins have been proposed successively.

Results

In order to achieve higher prediction performance, in this paper, a new prediction model called TGSO is proposed. In TGSO, a protein aggregation degree network is constructed first by adopting the node density measurement method for complex networks. And simultaneously, a protein co-expression interactive network is constructed by combining the gene expression information with the network connectivity, and a protein co-localization interaction network is constructed based on the subcellular localization data. And then, through integrating these three kinds of newly constructed networks, a comprehensive protein–protein interaction network will be obtained. Finally, based on the homology information, scores can be calculated out iteratively for different proteins, which can be utilized to estimate the importance of proteins effectively. Moreover, in order to evaluate the identification performance of TGSO, we have compared TGSO with 13 different latest competitive methods based on three kinds of yeast databases. And experimental results show that TGSO can achieve identification accuracies of 94%, 82% and 72% out of the top 1%, 5% and 10% candidate proteins respectively, which are to some degree superior to these state-of-the-art competitive models.

Conclusions

We constructed a comprehensive interactive network based on multi-source data to reduce the noise and errors in the initial PPI, and combined with iterative methods to improve the accuracy of necessary protein prediction, and means that TGSO may be conducive to the future development of essential protein recognition as well.

Celiac Disease Detection From Videocapsule Endoscopy Images Using Strip Principal Component Analysis

The purpose of this study was to implement principal component analysis (PCA) on videocapsule endoscopy (VE) images to develop a new computerized tool for celiac disease recognition. Three PCA algorithms were implemented for feature extraction and sparse representation. A novel strip PCA (SPCA) with nongreedy L1-norm maximization is proposed for VE image analysis. The extracted principal components were interpreted by a non-parametric k-nearest neighbor (k-NN) method for automated celiac disease classification. A benchmark dataset of 460 images (240 from celiac disease patients with small intestinal villous atrophy versus 220 control patients lacking villous atrophy) was constructed from the clinical VE series. It was found that the newly developed SPCA with nongreedy L1-norm maximization was most efficient for computerized celiac disease recognition, having a robust performance with an average recognition accuracy of 93.9 percent. Furthermore, SPCA also has a reduced computation time as compared with other methods. Therefore, it is likely that SPCA will be a helpful adjunct for the diagnosis of celiac disease.

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.

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.

Potential Pathogenic Genes Prioritization Based on Protein Domain Interaction Network Analysis

Pathogenicity-related studies are of great importance in understanding the pathogenesis of complex diseases and improving the level of clinical medicine. This work proposed a bioinformatics scheme to analyze cancer-related gene mutations, and try to figure out potential genes associated with diseases from the protein domain-domain interaction network. Herein, five measures of the principle of centrality lethality had been adopted to implement potential correlation analysis, and prioritize the significance of genes. This method was further applied to KEGG pathway analysis by taking the malignant melanoma as an example. The experimental results show that 25 domains can be found, and 18 of them have high potential to be pathogenically important related to malignant melanoma. Finally, a web-based tool, named Human Cancer Related Domain Interaction Network Analyzer, is developed for potential pathogenic genes prioritization for 26 types of human cancers, and the analysis results can be visualized and downloaded online.

Imbalance Data Processing Strategy for Protein Interaction Sites Prediction

Protein-protein interactions play essential roles in various biological progresses. Identifying protein interaction sites can facilitate researchers to understand life activities and therefore will be helpful for drug design. However, the number of experimental determined protein interaction sites is far less than that of protein sites in protein-protein interaction or protein complexes. Therefore, the negative and positive samples are usually imbalanced, which is common but bring result bias on the prediction of protein interaction sites by computational approaches. In this work, we presented three imbalance data processing strategies to reconstruct the original dataset, and then extracted protein features from the evolutionary conservation of amino acids to build a predictor for identification of protein interaction sites. On a dataset with 10,430 surface residues but only 2,299 interface residues, the imbalance dataset processing strategies can obviously reduce the prediction bias, and therefore improve the prediction performance of protein interaction sites. The experimental results show that our prediction models can achieve a better prediction performance, such as a prediction accuracy of 0.758, or a high F-measure of 0.737, which demonstrated the effectiveness of our method.

A Novel Approach for Potential Human LncRNA-Disease Association Prediction Based on Local Random Walk

In recent years, lncRNAs (long non-coding RNAs) have been proved to be closely related to many diseases that are seriously harmful to human health. Although researches on clarifying the relationships between lncRNAs and diseases are developing rapidly, associations between the lncRNAs and diseases are still remaining largely unknown. In this manuscript, a novel Local Random Walk based prediction model called LRWHLDA is proposed for inferring potential associations between human lncRNAs and diseases. In LRWHLDA, a new heterogeneous network is established first, which allows that LRWHLDA can be implemented in the case of lacking known lncRNA-disease associations. And then, an improved local random walk method is designed for prediction of novel lncRNA-disease associations, which can help LRWHLDA achieve high prediction accuracy but with low time complexity. Finally, in order to evaluate the prediction performance of LRWHLDA, different frameworks such as LOOCV, 2-folds CV, and 5-folds CV have been implemented, simulation results indicate that LRWHLDA can achieve reliable AUCs of 0.8037, 0.8354, and 0.8556 under the frameworks of 2-fold CV, 5-fold CV, and LOOCV, respectively. Hence, it is easy to know that LRWHLDA contains the potential to be a representative of emerging methods in the field of research on potential lncRNA-disease associations prediction.

Multi-Label Fusion Collaborative Matrix Factorization for Predicting LncRNA-Disease Associations

As we all know, science and technology are developing faster and faster. Many experts and scholars have demonstrated that human diseases are related to lncRNA, but only a few associations have been confirmed, and many unknown associations need to be found. In the process of finding associations, it takes a lot of time, so finding an efficient way to predict the associations between lncRNAs and diseases is particularly important. In this paper, we propose a multi-label fusion collaborative matrix factorization (MLFCMF) approach for predicting lncRNA-disease associations (LDAs). Firstly, the lncRNA space and disease space are optimized by multi-label to enhance the intrinsic link between lncRNA and disease and to tap potential information. Multi-label learning can encode a variety of data information from the sample space. Secondly, to learn multi-label information in the data space, the fusion method is used to handle the relationship between multiple labels. More comprehensive information will be obtained by weighing the effects of different labels. The addition of Gaussian interaction profile (GIP) kernel can increase the network similarity. Finally, the lncRNA-disease associations are predicted by the method of collaborative matrix factorization. The ten-fold cross-validation method is used to evaluate the MLFCMF method, and our method finally obtains an AUC value of 0.8612. Detailed analysis of ovarian cancer, colorectal cancer, and lung cancer in the simulation experiment results. So it can be seen that our method MLFCMF is an effective model for predicting lncRNA-disease associations.

A comprehensive survey on computational methods of non-coding RNA and disease association prediction

The studies on relationships between non-coding RNAs and diseases are widely carried out in recent years. A large number of experimental methods and technologies of producing biological data have also been developed. However, due to their high labor cost and production time, nowadays, calculation-based methods, especially machine learning and deep learning methods, have received a lot of attention and been used commonly to solve these problems. From a computational point of view, this survey mainly introduces three common non-coding RNAs, i.e. miRNAs, lncRNAs and circRNAs, and the related computational methods for predicting their association with diseases. First, the mainstream databases of above three non-coding RNAs are introduced in detail. Then, we present several methods for RNA similarity and disease similarity calculations. Later, we investigate ncRNA-disease prediction methods in details and classify these methods into five types: network propagating, recommend system, matrix completion, machine learning and deep learning. Furthermore, we provide a summary of the applications of these five types of computational methods in predicting the associations between diseases and miRNAs, lncRNAs and circRNAs, respectively. Finally, the advantages and limitations of various methods are identified, and future researches and challenges are also discussed.

Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives

Long non-coding RNAs (lncRNA), the pervasively transcribed part of the mammalian genome, have played a significant role in changing our protein-centric view of genomes. The abundance of lncRNAs and their diverse roles across cell types have opened numerous avenues for the research community regarding lncRNAome. To discover and understand lncRNAome, many sophisticated computational techniques have been leveraged. Recently, deep learning (DL)-based modeling techniques have been successfully used in genomics due to their capacity to handle large amounts of data and produce relatively better results than traditional machine learning (ML) models. DL-based modeling techniques have now become a choice for many modeling tasks in the field of lncRNAome as well. In this review article, we summarized the contribution of DL-based methods in nine different lncRNAome research areas. We also outlined DL-based techniques leveraged in lncRNAome, highlighting the challenges computational scientists face while developing DL-based models for lncRNAome. To the best of our knowledge, this is the first review article that summarizes the role of DL-based techniques in multiple areas of lncRNAome.

LDNFSGB: prediction of long non-coding rna and disease association using network feature similarity and gradient boosting

BACKGROUND

A large number of experimental studies show that the mutation and regulation of long non-coding RNAs (lncRNAs) are associated with various human diseases. Accurate prediction of lncRNA-disease associations can provide a new perspective for the diagnosis and treatment of diseases. The main function of many lncRNAs is still unclear and using traditional experiments to detect lncRNA-disease associations is time-consuming.

RESULTS

In this paper, we develop a novel and effective method for the prediction of lncRNA-disease associations using network feature similarity and gradient boosting (LDNFSGB). In LDNFSGB, we first construct a comprehensive feature vector to effectively extract the global and local information of lncRNAs and diseases through considering the disease semantic similarity (DISSS), the lncRNA function similarity (LNCFS), the lncRNA Gaussian interaction profile kernel similarity (LNCGS), the disease Gaussian interaction profile kernel similarity (DISGS), and the lncRNA-disease interaction (LNCDIS). Particularly, two methods are used to calculate the DISSS (LNCFS) for considering the local and global information of disease semantics (lncRNA functions) respectively. An autoencoder is then used to reduce the dimensionality of the feature vector to obtain the optimal feature parameter from the original feature set. Furthermore, we employ the gradient boosting algorithm to obtain the lncRNA-disease association prediction.

CONCLUSIONS

In this study, hold-out, leave-one-out cross-validation, and ten-fold cross-validation methods are implemented on three publicly available datasets to evaluate the performance of LDNFSGB. Extensive experiments show that LDNFSGB dramatically outperforms other state-of-the-art methods. The case studies on six diseases, including cancers and non-cancers, further demonstrate the effectiveness of our method in real-world applications.

PrGeFNE: Predicting disease-related genes by fast network embedding

Identifying disease-related genes is of importance for understanding of molecule mechanisms of diseases, as well as diagnosis and treatment of diseases. Many computational methods have been proposed to predict disease-related genes, but how to make full use of multi-source biological data to enhance the ability of disease-gene prediction is still challenging. In this paper, we proposed a novel method for predicting disease-related genes by using fast network embedding (PrGeFNE), which can integrate multiple types of associations related to diseases and genes. Specifically, we first constructed a heterogeneous network by using phenotype-disease, disease-gene, protein-protein and gene-GO associations; and low-dimensional representation of nodes is extracted from the network by using a fast network embedding algorithm. Then, a dual-layer heterogeneous network was reconstructed by using the low-dimensional representation, and a network propagation was applied to the dual-layer heterogeneous network to predict disease-related genes. Through cross-validation and newly added-association validation, we displayed the important roles of different types of association data in enhancing the ability of disease-gene prediction, and confirmed the excellent performance of PrGeFNE by comparing to state-of-the-art algorithms. Furthermore, we developed a web tool that can facilitate researchers to search for candidate genes of different diseases predicted by PrGeFNE, along with the enrichment analysis of GO and pathway on candidate gene set. This may be useful for investigation of diseases' molecular mechanisms as well as their experimental validations. The web tool is available at http://bioinformatics.csu.edu.cn/prgefne/.

Attentional multi-level representation encoding based on convolutional and variance autoencoders for lncRNA-disease association prediction

As the abnormalities of long non-coding RNAs (lncRNAs) are closely related to various human diseases, identifying disease-related lncRNAs is important for understanding the pathogenesis of complex diseases. Most of current data-driven methods for disease-related lncRNA candidate prediction are based on diseases and lncRNAs. Those methods, however, fail to consider the deeply embedded node attributes of lncRNA-disease pairs, which contain multiple relations and representations across lncRNAs, diseases and miRNAs. Moreover, the low-dimensional feature distribution at the pairwise level has not been taken into account. We propose a prediction model, VADLP, to extract, encode and adaptively integrate multi-level representations. Firstly, a triple-layer heterogeneous graph is constructed with weighted inter-layer and intra-layer edges to integrate the similarities and correlations among lncRNAs, diseases and miRNAs. We then define three representations including node attributes, pairwise topology and feature distribution. Node attributes are derived from the graph by an embedding strategy to represent the lncRNA-disease associations, which are inferred via their common lncRNAs, diseases and miRNAs. Pairwise topology is formulated by random walk algorithm and encoded by a convolutional autoencoder to represent the hidden topological structural relations between a pair of lncRNA and disease. The new feature distribution is modeled by a variance autoencoder to reveal the underlying lncRNA-disease relationship. Finally, an attentional representation-level integration module is constructed to adaptively fuse the three representations for lncRNA-disease association prediction. The proposed model is tested over a public dataset with a comprehensive list of evaluations. Our model outperforms six state-of-the-art lncRNA-disease prediction models with statistical significance. The ablation study showed the important contributions of three representations. In particular, the improved recall rates under different top $k$ values demonstrate that our model is powerful in discovering true disease-related lncRNAs in the top-ranked candidates. Case studies of three cancers further proved the capacity of our model to discover potential disease-related lncRNAs.

Computational Methods and Applications for Identifying Disease-Associated lncRNAs as Potential Biomarkers and Therapeutic Targets

Long non-coding RNAs (lncRNAs) have been recognized as critical components of a broad genomic regulatory network and play pivotal roles in physiological and pathological processes. Identification of disease-associated lncRNAs is becoming increasingly crucial for fundamentally improving our understanding of molecular mechanisms of disease and developing novel biomarkers and therapeutic targets. Considering lower efficiency and higher time and labor cost of biological experiments, computer-aided inference of disease-associated RNAs has become a promising avenue for facilitating the study of lncRNA functions and provides complementary value for experimental studies. In this study, we first summarize data and knowledge resources publicly available for the study of lncRNA-disease associations. Then, we present an updated systematic overview of dozens of computational methods and models for inferring lncRNA-disease associations proposed in recent years. Finally, we explore the perspectives and challenges for further studies. Our study provides a guide for biologists and medical scientists to look for dedicated resources and more competent tools for accelerating the unraveling of disease-associated lncRNAs.

GBDTL2E: Predicting lncRNA-EF Associations Using Diffusion and HeteSim Features Based on a Heterogeneous Network

Interactions between genetic factors and environmental factors (EFs) play an important role in many diseases. Many diseases result from the interaction between genetics and EFs. The long non-coding RNA (lncRNA) is an important non-coding RNA that regulates life processes. The ability to predict the associations between lncRNAs and EFs is of important practical significance. However, the recent methods for predicting lncRNA-EF associations rarely use the topological information of heterogenous biological networks or simply treat all objects as the same type without considering the different and subtle semantic meanings of various paths in the heterogeneous network. In order to address this issue, a method based on the Gradient Boosting Decision Tree (GBDT) to predict the association between lncRNAs and EFs (GBDTL2E) is proposed in this paper. The innovation of the GBDTL2E integrates the structural information and heterogenous networks, combines the Hetesim features and the diffusion features based on multi-feature fusion, and uses the machine learning algorithm GBDT to predict the association between lncRNAs and EFs based on heterogeneous networks. The experimental results demonstrate that the proposed algorithm achieves a high performance.

A random forest based computational model for predicting novel lncRNA-disease associations

BACKGROUND

Accumulated evidence shows that the abnormal regulation of long non-coding RNA (lncRNA) is associated with various human diseases. Accurately identifying disease-associated lncRNAs is helpful to study the mechanism of lncRNAs in diseases and explore new therapies of diseases. Many lncRNA-disease association (LDA) prediction models have been implemented by integrating multiple kinds of data resources. However, most of the existing models ignore the interference of noisy and redundancy information among these data resources.

RESULTS

To improve the ability of LDA prediction models, we implemented a random forest and feature selection based LDA prediction model (RFLDA in short). First, the RFLDA integrates the experiment-supported miRNA-disease associations (MDAs) and LDAs, the disease semantic similarity (DSS), the lncRNA functional similarity (LFS) and the lncRNA-miRNA interactions (LMI) as input features. Then, the RFLDA chooses the most useful features to train prediction model by feature selection based on the random forest variable importance score that takes into account not only the effect of individual feature on prediction results but also the joint effects of multiple features on prediction results. Finally, a random forest regression model is trained to score potential lncRNA-disease associations. In terms of the area under the receiver operating characteristic curve (AUC) of 0.976 and the area under the precision-recall curve (AUPR) of 0.779 under 5-fold cross-validation, the performance of the RFLDA is better than several state-of-the-art LDA prediction models. Moreover, case studies on three cancers demonstrate that 43 of the 45 lncRNAs predicted by the RFLDA are validated by experimental data, and the other two predicted lncRNAs are supported by other LDA prediction models.

CONCLUSIONS

Cross-validation and case studies indicate that the RFLDA has excellent ability to identify potential disease-associated lncRNAs.

Probing lncRNA-Protein Interactions: Data Repositories, Models, and Algorithms

Identifying lncRNA-protein interactions (LPIs) is vital to understanding various key biological processes. Wet experiments found a few LPIs, but experimental methods are costly and time-consuming. Therefore, computational methods are increasingly exploited to capture LPI candidates. We introduced relevant data repositories, focused on two types of LPI prediction models: network-based methods and machine learning-based methods. Machine learning-based methods contain matrix factorization-based techniques and ensemble learning-based techniques. To detect the performance of computational methods, we compared parts of LPI prediction models on Leave-One-Out cross-validation (LOOCV) and fivefold cross-validation. The results show that SFPEL-LPI obtained the best performance of AUC. Although computational models have efficiently unraveled some LPI candidates, there are many limitations involved. We discussed future directions to further boost LPI predictive performance.

IDLDA: An Improved Diffusion Model for Predicting LncRNA-Disease Associations

It has been demonstrated that long non-coding RNAs (lncRNAs) play important roles in a variety of biological processes associated with human diseases. However, the identification of lncRNA–disease associations by experimental methods is time-consuming and labor-intensive. Computational methods provide an effective strategy to predict more potential lncRNA–disease associations to some degree. Based on the hypothesis that phenotypically similar diseases are often associated with functionally similar lncRNAs and vice versa, we developed an improved diffusion model to predict potential lncRNA–disease associations (IDLDA). As a result, our model performed well in the global and local cross-validations, which indicated that IDLDA had a great performance in predicting novel associations. Case studies of colon cancer, breast cancer, and gastric cancer were also implemented, all lncRNAs which ranked top 10 in both databases were verified by databases and related literature. The results showed that IDLDA might play a key role in biomedical research.

A novel target convergence set based random walk with restart for prediction of potential LncRNA-disease associations

BACKGROUND

In recent years, lncRNAs (long-non-coding RNAs) have been proved to be closely related to the occurrence and development of many serious diseases that are seriously harmful to human health. However, most of the lncRNA-disease associations have not been found yet due to high costs and time complexity of traditional bio-experiments. Hence, it is quite urgent and necessary to establish efficient and reasonable computational models to predict potential associations between lncRNAs and diseases.

RESULTS

In this manuscript, a novel prediction model called TCSRWRLD is proposed to predict potential lncRNA-disease associations based on improved random walk with restart. In TCSRWRLD, a heterogeneous lncRNA-disease network is constructed first by combining the integrated similarity of lncRNAs and the integrated similarity of diseases. And then, for each lncRNA/disease node in the newly constructed heterogeneous lncRNA-disease network, it will establish a node set called TCS (Target Convergence Set) consisting of top 100 disease/lncRNA nodes with minimum average network distances to these disease/lncRNA nodes having known associations with itself. Finally, an improved random walk with restart is implemented on the heterogeneous lncRNA-disease network to infer potential lncRNA-disease associations. The major contribution of this manuscript lies in the introduction of the concept of TCS, based on which, the velocity of convergence of TCSRWRLD can be quicken effectively, since the walker can stop its random walk while the walking probability vectors obtained by it at the nodes in TCS instead of all nodes in the whole network have reached stable state. And Simulation results show that TCSRWRLD can achieve a reliable AUC of 0.8712 in the Leave-One-Out Cross Validation (LOOCV), which outperforms previous state-of-the-art results apparently. Moreover, case studies of lung cancer and leukemia demonstrate the satisfactory prediction performance of TCSRWRLD as well.

CONCLUSIONS

Both comparative results and case studies have demonstrated that TCSRWRLD can achieve excellent performances in prediction of potential lncRNA-disease associations, which imply as well that TCSRWRLD may be a good addition to the research of bioinformatics in the future.

Hierarchical Extension Based on the Boolean Matrix for LncRNADisease Association Prediction

BACKGROUND

Accumulating experimental studies demonstrated that long non-coding RNAs (LncRNAs) play crucial roles in the occurrence and development progress of various complex human diseases. Nonetheless, only a small portion of LncRNA-disease associations have been experimentally verified at present. Automatically predicting LncRNA-disease associations based on computational models can save the huge cost of wet-lab experiments.

METHODS AND RESULT

To develop effective computational models to integrate various heterogeneous biological data for the identification of potential disease-LncRNA, we propose a hierarchical extension based on the Boolean matrix for LncRNA-disease association prediction model (HEBLDA). HEBLDA discovers the intrinsic hierarchical correlation based on the property of the Boolean matrix from various relational sources. Then, HEBLDA integrates these hierarchical associated matrices by fusion weights. Finally, HEBLDA uses the hierarchical associated matrix to reconstruct the LncRNA- disease association matrix by hierarchical extending. HEBLDA is able to work for potential diseases or LncRNA without known association data. In 5-fold cross-validation experiments, HEBLDA obtained an area under the receiver operating characteristic curve (AUC) of 0.8913, improving previous classical methods. Besides, case studies show that HEBLDA can accurately predict candidate disease for several LncRNAs.

CONCLUSION

Based on its ability to discover the more-richer correlated structure of various data sources, we can anticipate that HEBLDA is a potential method that can obtain more comprehensive association prediction in a broad field.

Inferring disease and pathway associations of long non-coding RNAs using heterogeneous information network model

Recent findings from biological experiments demonstrate that long non-coding RNAs (lncRNAs) are actively involved in critical cellular processes and are associated with innumerable diseases. Computational prediction of lncRNA-disease association draws tremendous research attention nowadays. This paper proposes a machine learning model that predicts lncRNA-disease associations using Heterogeneous Information Network (HIN) of lncRNAs and diseases. A Support Vector Machine classifier is developed using the feature set extracted from a meta-path-based parameter, Association Index derived from the HIN. Performance of the model is validated using standard statistical metrics and it generated an AUC value of 0.87, which is better than the existing methods in the literature. Results are further validated using the recent literature and many of the predicted lncRNA-disease associations are identified as actually existing. This paper also proposes an HIN-based methodology to associate lncRNAs with pathways in which they may have biological influence. A case study on the pathway associations of four well-known lncRNAs (HOTAIR, TUG1, NEAT1, and MALAT1) has been conducted. It has been observed that many times the same lncRNA is associated with more than one biologically related pathways. Further exploration is needed to substantiate whether such lncRNAs have any role in determining the pathway interplay. The script and sample data for the model construction is freely available at http://bdbl.nitc.ac.in/LncDisPath/index.html.