Bi-random Walk Research Articles

Drug repositioning by collaborative learning based on graph convolutional inductive network

Motivation:Computational drug repositioning is a vital path to improve efficiency of drug discovery, which aims to find potential Drug–Disease Associations (DDAs) to develop new effects of the existing drugs. Many approaches detected novel DDAs from heterogenous network which integrates similar drugs, similar diseases and the known DDAs. However, sparsity of the known DDAs and intrinsic synergic relations on representations of drugs and diseases in the heterogenous network are still the main challenges for DDAs prediction. Results:To address the problems, a novel drug repositioning approach is proposed here. Firstly, a drug similar network is constructed by Gaussian similarity kernel fusion of multisource drug similarities. Likewise, a disease similar network is generated by the same strategy. Secondly, the known DDAs network is extended by a bi-random walk algorithm from the above-mentioned similar networks. Meanwhile, representations of drugs and diseases are learned from their similar networks through graph convolutions and then a DDAs network is induced from the representations. Finally, to discover latent DDAs, the inductive DDAs network is refined iteratively by collaborating with the extended known DDAs network. Comprehensive experimental results show that our method outperforms several state-of-the-art methods for predicting DDAs on indicators including precision, recall, F1-score, MCC, ROC and AUPR. Moreover, case studies suggest that our method is highly effective in practices. The success of our method may be attributed to three aspects: (1) reliable similar relationships of drugs and diseases; (2) enhanced connectivity of the heterogenous network; (3) reasonable collaborative induction on DDAs network. Our method is freely available at https://github.com/BioMLab/DRCLN.

CircRNA-disease association prediction with an improved unbalanced Bi-Random walk

Studies show that circular RNAs (circRNAs), a type of non-coding RNAs, play various roles in biological processes such as the formation and progression of many different diseases. For this reason, identifying potential circRNAs associated with diseases is vital for early diagnosis. Determining these relationships experimentally requires a long process and is also expensive. For this reason, computational models are being developed to determine the relationships between circRNA and diseases. In this study, we recommend a technique called Improved Unbalanced Bi-Random Walk (UBRW) to identify potential circRNAs associated with diseases. The commonly used 5-fold cross-validation (CV) technique and leave one-out cross-validation (LOOCV) technique were applied to verify the predictive ability of our technique. The area under curve (AUC) values calculated in 5-fold CV and LOOCV are 0.8910 and 0.9669, respectively. Case studies on the commonly occurring gastric cancer and breast cancer were conducted to further validate the predictive performance of our method. When the results were examined, it was seen that the prediction ability of the UBRW method was quite successful.

Traditional Chinese Medicine studies for Alzheimer's disease via network pharmacology based on entropy and random walk.

Alzheimer's disease (AD) is a common neurodegenerative disease having complex pathogenesis, approved drugs can only alleviate symptoms of AD for a period of time. Traditional Chinese medicine (TCM) contains multiple active ingredients that can act on multiple targets simultaneously. In this paper, a novel algorithm based on entropy and random walk with the restart of heterogeneous network (RWRHE) is proposed for predicting active ingredients for AD and screening out the effective TCMs for AD. First, Six TCM compounds containing 20 herbs from the AD drug reviews in the CNKI (China National Knowledge Internet) are collected, their active ingredients and targets are retrieved from different databases. Then, comprehensive similarity networks of active ingredients and targets are constructed based on different aspects and entropy weight, respectively. A comprehensive heterogeneous network is constructed by integrating the known active ingredient-target association information and two comprehensive similarity networks. Subsequently, bi-random walks are applied on the heterogeneous network to predict active ingredient-target associations. AD related targets are selected as the seed nodes, a random walk is carried out on the target similarity network to predict the AD-target associations, and the associations of AD-active ingredients are inferred and scored. The effective herbs and compounds for AD are screened out based on their active ingredients' scores. The results measured by machine learning and bioinformatics show that the RWRHE algorithm achieves better prediction accuracy, the top 15 active ingredients may act as multi-target agents in the prevention and treatment of AD, Danshen, Gouteng and Chaihu are recommended as effective TCMs for AD, Yiqitongyutang is recommended as effective compound for AD.

LPI-IBWA: Predicting lncRNA-protein interactions based on an improved Bi-Random walk algorithm

Many studies have shown that long-chain noncoding RNAs (lncRNAs) are involved in a variety of biological processes such as post-transcriptional gene regulation, splicing, and translation by combining with corresponding proteins. Predicting lncRNA-protein interactions is an effective approach to infer the functions of lncRNAs. The paper proposes a new computational model named LPI-IBWA. At first, LPI-IBWA uses similarity kernel fusion (SKF) to integrate various types of biological information to construct lncRNA and protein similarity networks. Then, a bounded matrix completion model and a weighted k-nearest known neighbors algorithm are utilized to update the initial sparse lncRNA-protein interaction matrix. Based on the updated lncRNA-protein interaction matrix, the lncRNA similarity network and the protein similarity network are integrated into a heterogeneous network. Finally, an improved Bi-Random walk algorithm is used to predict novel latent lncRNA-protein interactions. 5-fold cross-validation experiments on a benchmark dataset showed that the AUC and AUPR of LPI-IBWA reach 0.920 and 0.736, respectively, which are higher than those of other state-of-the-art methods. Furthermore, the experimental results of case studies on a novel dataset also illustrated that LPI-IBWA could efficiently predict potential lncRNA-protein interactions.

Multi-View Feature Aggregation for Predicting Microbe-Disease Association.

Microbes play a crucial role in human health and disease. Figuring out the relationship between microbes and diseases leads to significant potential applications in disease treatments. It is an urgent need to devise robust and effective computational methods for identifying disease-related microbes. This work proposes a Multi-View Feature Aggregation (MVFA) scheme that integrates the linear and nonlinear features to identify disease-related microbes. We introduce a non-negative matrix tri-factorization (NMTF) model to extract linear features for diseases and microbes. Then we learn another type of linear feature by utilizing a bi-random walk model. The nonlinear feature is obtained by inputting the two kinds of linear features into a capsule neural network. These three types of features describe the associations between diseases and microbes from different views. Finally, considering the complementary of these features, we leverage a logistic regression model to combine the NMTF model predictions, bi-random walk model predictions, and the capsule neural network predictions to obtain the final microbe-disease pair scores. We apply our method to predict human microbe-disease associations on two datasets. Experimental results show that our multi-view model outperforms the state-of-the-art models in recovering missing microbe-disease associations and predicting associations for new microbes. The ablation study shows that aggregating multi-view linear and nonlinear features can improve the prediction performance. Case studies on two diseases, i.e. Type 1 diabetes and Liver cirrhosis, further validate our method effectiveness.

NTBiRW: A Novel Neighbor Model based on Two-tier Bi-Random Walk for Predicting Potential Disease-related Microbes.

Studies have revealed that microbes have an important effect on numerous physiological processes, and further research on the links between diseases and microbes is significant. Given that laboratory methods are expensive and not optimized, computational models are increasingly used for discovering disease-related microbes. Here, a new neighbor approach based on two-tier Bi-Random Walk is proposed for potential disease-related microbes, known as NTBiRW. In this method, the first step is to construct multiple microbe similarities and disease similarities. Then, three kinds of microbe/disease similarity are integrated through two-tier Bi-Random Walk to obtain the final integrated microbe/disease similarity network with different weights. Finally, Weighted K Nearest Known Neighbors (WKNKN) is used for prediction based on the final similarity network. In addition, leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold CV) are applied for evaluating the performance of NTBiRW. Multiple evaluating indicators are taken to show the performance from multiple perspectives. And most of the evaluation index values of NTBiRW are better than those of the compared methods. Moreover, in case studies on atopic dermatitis and psoriasis, most of the first 10 candidates in the final result can be proven. This also demonstrates the capability of NTBiRW for discovering new associations. Therefore, this method can contribute to the discovery of disease-related microbes and thus offer new thoughts for further understanding the pathogenesis of diseases.

An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks.

The integration of microarray technologies and machine learning methods has become popular in predicting the pathological condition of diseases and discovering risk genes. Traditional microarray analysis considers pathways as a simple gene set, treating all genes in the pathway identically while ignoring the pathway network's structure information. This study proposed an entropy-based directed random walk (e-DRW) method to infer pathway activities. Two enhancements from the conventional DRW were conducted, which are (1) to increase the coverage of human pathway information by constructing two inputting networks for pathway activity inference, and (2) to enhance the gene-weighting method in DRW by incorporating correlation coefficient values and t-test statistic scores. To test the objectives, gene expression datasets were used as input datasets while the pathway datasets were used as reference datasets to build two directed graphs. The within-dataset experiments indicated that e-DRW method demonstrated robust and superior performance in terms of classification accuracy and robustness of the predicted risk-active pathways compared to the other methods. In conclusion, the results revealed that e-DRW not only improved the prediction performance, but also effectively extracted topologically important pathways and genes that were specifically related to the corresponding cancer types.

BRWMC: Predicting lncRNA-disease associations based on bi-random walk and matrix completion on disease and lncRNA networks

Many experiments have proved that long non-coding RNAs (lncRNAs) in humans have been implicated in disease development. The prediction of lncRNA-disease association is essential in promoting disease treatment and drug development. It is time-consuming and laborious to explore the relationship between lncRNA and diseases in the laboratory. The computation-based approach has clear advantages and has become a promising research direction. This paper proposes a new lncRNA disease association prediction algorithm BRWMC. Firstly, BRWMC constructed several lncRNA (disease) similarity networks based on different measurement angles and fused them into an integrated similarity network by similarity network fusion (SNF). In addition, the random walk method is used to preprocess the known lncRNA-disease association matrix and calculate the estimated scores of potential lncRNA-disease associations. Finally, the matrix completion method accurately predicts the potential lncRNA-disease associations. Under the framework of leave-one-out cross-validation and 5-fold cross-validation, the AUC values obtained by BRWMC are 0.9610 and 0.9739, respectively. In addition, case studies of three common diseases show that BRWMC is a reliable method for prediction.

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.

Finding Lung-Cancer-Related lncRNAs Based on Laplacian Regularized Least Squares With Unbalanced Bi-Random Walk.

Lung cancer is one of the leading causes of cancer-related deaths. Thus, it is important to find its biomarkers. Furthermore, there is an increasing number of studies reporting that long noncoding RNAs (lncRNAs) demonstrate dense linkages with multiple human complex diseases. Inferring new lncRNA-disease associations help to identify potential biomarkers for lung cancer and further understand its pathogenesis, design new drugs, and formulate individualized therapeutic options for lung cancer patients. This study developed a computational method (LDA-RLSURW) by integrating Laplacian regularized least squares and unbalanced bi-random walk to discover possible lncRNA biomarkers for lung cancer. First, the lncRNA and disease similarities were computed. Second, unbalanced bi-random walk was, respectively, applied to the lncRNA and disease networks to score associations between diseases and lncRNAs. Third, Laplacian regularized least squares were further used to compute the association probability between each lncRNA-disease pair based on the computed random walk scores. LDA-RLSURW was compared using 10 classical LDA prediction methods, and the best AUC value of 0.9027 on the lncRNADisease database was obtained. We found the top 30 lncRNAs associated with lung cancers and inferred that lncRNAs TUG1, PTENP1, and UCA1 may be biomarkers of lung neoplasms, non-small–cell lung cancer, and LUAD, respectively.

HBRWRLDA: predicting potential lncRNA-disease associations based on hypergraph bi-random walk with restart.

Accumulating evidence indicates that the regulation of long non-coding RNAs (lncRNAs) is closely related to a variety of diseases. Identifying meaningful lncRNA-disease associations will help to contribute to the understanding of the molecular mechanisms underlying these diseases. However, only a limited number of associations between lncRNAs and diseases have been inferred from traditional biological experiments due to the high cost and highly specialized. Therefore, computational methods are increasingly used to reduce time of biological experiments and complement biological research. In this paper, a computational method called HBRWRLDA is proposed to predict lncRNA-disease associations. First, HBRWRLDA models the relationships between multiple nodes using hypergraphs, which allows HBRWRLDA to integrate the expression similarity of lncRNAs and the semantic similarity of diseases to construct hypergraphs. Then, a bi-random walk on hypergraphs is used to predict potential lncRNA-disease associations. HBRWRLDA achieves a higher area under the curve value of 0.9551 and [Formula: see text], respectively, compared with the other five advanced methods under the framework of one-leave cross validation (LOOCV) and five-fold cross-validation (5-fold CV). In addition, the prediction effect of HBRWRLDA was confirmed case studies of three diseases: renal cell carcinoma, gastric cancer, and hepatocellular carcinoma. Case studies demonstrates the capacity of HBRWRLDA to identify potentially disease-associated lncRNAs. Overall, HBRWRLDA is excellent at predicting potential lncRNA-disease associations and could be useful in conducting further biological experiments by helping researchers identify candidates of lncRNA-disease association.

A tensor-based bi-random walks model for protein function prediction

BackgroundThe accurate characterization of protein functions is critical to understanding life at the molecular level and has a huge impact on biomedicine and pharmaceuticals. Computationally predicting protein function has been studied in the past decades. Plagued by noise and errors in protein–protein interaction (PPI) networks, researchers have undertaken to focus on the fusion of multi-omics data in recent years. A data model that appropriately integrates network topologies with biological data and preserves their intrinsic characteristics is still a bottleneck and an aspirational goal for protein function prediction.ResultsIn this paper, we propose the RWRT (Random Walks with Restart on Tensor) method to accomplish protein function prediction by applying bi-random walks on the tensor. RWRT firstly constructs a functional similarity tensor by combining protein interaction networks with multi-omics data derived from domain annotation and protein complex information. After this, RWRT extends the bi-random walks algorithm from a two-dimensional matrix to the tensor for scoring functional similarity between proteins. Finally, RWRT filters out possible pretenders based on the concept of cohesiveness coefficient and annotates target proteins with functions of the remaining functional partners. Experimental results indicate that RWRT performs significantly better than the state-of-the-art methods and improves the area under the receiver-operating curve (AUROC) by no less than 18%.ConclusionsThe functional similarity tensor offers us an alternative, in that it is a collection of networks sharing the same nodes; however, the edges belong to different categories or represent interactions of different nature. We demonstrate that the tensor-based random walk model can not only discover more partners with similar functions but also free from the constraints of errors in protein interaction networks effectively. We believe that the performance of function prediction depends greatly on whether we can extract and exploit proper functional similarity information on protein correlations.

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.

VDA-RWLRLS: An anti-SARS-CoV-2 drug prioritizing framework combining an unbalanced bi-random walk and Laplacian regularized least squares

BackgroundA new coronavirus disease named COVID-19, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is rapidly spreading worldwide. However, there is currently no effective drug to fight COVID-19. MethodsIn this study, we developed a Virus-Drug Association (VDA) identification framework (VDA-RWLRLS) combining unbalanced bi-Random Walk, Laplacian Regularized Least Squares, molecular docking, and molecular dynamics simulation to find clues for the treatment of COVID-19. First, virus similarity and drug similarity are computed based on genomic sequences, chemical structures, and Gaussian association profiles. Second, an unbalanced bi-random walk is implemented on the virus network and the drug network, respectively. Third, the results of the random walks are taken as the input of Laplacian regularized least squares to compute the association score for each virus-drug pair. Fourth, the final associations are characterized by integrating the predictions from the virus network and the drug network. Finally, molecular docking and molecular dynamics simulation are implemented to measure the potential of screened anti-COVID-19 drugs and further validate the predicted results. ResultsIn comparison with six state-of-the-art association prediction models (NGRHMDA, SMiR-NBI, LRLSHMDA, VDA-KATZ, VDA-RWR, and VDA-BiRW), VDA-RWLRLS demonstrates superior VDA prediction performance. It obtains the best AUCs of 0.885 8, 0.835 5, and 0.862 5 on the three VDA datasets. Molecular docking and dynamics simulations demonstrated that remdesivir and ribavirin may be potential anti-COVID-19 drugs. ConclusionsIntegrating unbalanced bi-random walks, Laplacian regularized least squares, molecular docking, and molecular dynamics simulation, this work initially screened a few anti-SARS-CoV-2 drugs and may contribute to preventing COVID-19 transmission.

Logistic Weighted Profile-Based Bi-Random Walk for Exploring MiRNA-Disease Associations

MicroRNAs (miRNAs) exert an enormous influence on cell differentiation, biological development and the onset of diseases. Because predicting potential miRNA-disease associations (MDAs) by biological experiments usually requires considerable time and money, a growing number of researchers are working on developing computational methods to predict MDAs. High accuracy is critical for prediction. To date, many algorithms have been proposed to infer novel MDAs. However, they may still have some drawbacks. In this paper, a logistic weighted profile-based bi-random walk method (LWBRW) is designed to infer potential MDAs based on known MDAs. In this method, three networks (i.e., a miRNA functional similarity network, a disease semantic similarity network and a known MDA network) are constructed first. In the process of building the miRNA network and the disease network, Gaussian interaction profile (GIP) kernel is computed to increase the kernel similarities, and the logistic function is used to extract valuable information and protect known MDAs. Next, the known MDA matrix is preprocessed by the weighted K-nearest known neighbours (WKNKN) method to reduce the number of false negatives. Then, the LWBRW method is applied to infer novel MDAs by bi-randomly walking on the miRNA network and the disease network. Finally, the predictive ability of the LWBRW method is confirmed by the average AUC of 0.939 3 (0.006 1) in 5-fold cross-validation (CV) and the AUC value of 0.976 3 in leave-one-out cross-validation (LOOCV). In addition, case studies also show the outstanding ability of the LWBRW method to explore potential MDAs.

Fusing Multiple Biological Networks to Effectively Predict miRNA-disease Associations

Background: MicroRNAs (miRNAs) are a class of endogenous non-coding RNAs with about 22 nucleotides, and they play a significant role in a variety of complex biological processes. Many researches have shown that miRNAs are closely related to human diseases. Although the biological experiments are reliable in identifying miRNA-disease associations, they are timeconsuming and costly. Objective: Thus, computational methods are urgently needed to effectively predict miRNA-disease associations. Methods: In this paper, we proposed a novel method, BIRWMDA, based on a bi-random walk model to predict miRNA-disease associations. Specifically, in BIRWMDA, the similarity network fusion algorithm is used to combine the multiple similarity matrices to obtain a miRNA-miRNA similarity matrix and a disease-disease similarity matrix, then the miRNA-disease associations were predicted by the bi-random walk model. Results: To evaluate the performance of BIRWMDA, we ran the leave-one-out cross-validation and 5-fold cross-validation, and their corresponding AUCs were 0.9303 and 0.9223 ± 0.00067, respectively. To further demonstrate the effectiveness of the BIRWMDA, from the perspective of exploring disease-related miRNAs, we conducted three case studies of breast neoplasms, prostate neoplasms and gastric neoplasms, where 48, 50 and 50 out of the top 50 predicted miRNAs were confirmed by literature, respectively. From the perspective of exploring miRNA-related diseases, we conducted two case studies of hsa-mir-21 and hsa-mir-155, where 7 and 5 out of the top 10 predicted diseases were confirmed by literatures, respectively. Conclusion: The fusion of multiple biological networks could effectively predict miRNA-diseases associations. We expected BIRWMDA to serve as a biological tool for mining potential miRNAdisease associations.

Prediction of Virus-Receptor Interactions Based on Improving Similarities.

Viral infectious diseases have been seriously threatening human health. The receptor binding is the first step of viral infection. Predicting virus-receptor interactions will be helpful for the interaction mechanism of viruses and receptors, and further find some effective ways of preventing and treating viral infectious diseases so as to reduce the morbidity and mortality caused by viruses. Some computation algorithms have been proposed for identifying potential virus-receptor interactions. However, a common problem in those methods is the presence of noise in the similarity network. A new computational model (Network Enhancement and the Regularized Least Squares [NERLS]) is proposed to predict virus-receptor interactions based on improving similarities by Network Enhancement (NE). NERLS integrates the virus sequence similarity, the receptor sequence similarity and known virus-receptor interactions. We compute the virus sequence similarity and known virus-receptor interactions to construct the virus similarity network. The receptor similarity network is constructed by the Gaussian interaction profile kernel similarity and the receptor sequence similarity. To obtain the final virus similarity network and the final receptor similarity network, NE is, respectively, applied for reducing the noise of the virus similarity network and the receptor similarity network. Finally, NERLS employs the regularized least squares to predict interactions of viruses and receptors. The experiment results show that NERLS achieves the area under curve value of 0.893 and 0.921 in 10-fold cross-validation and leave-one-out cross-validation, respectively, which is consistently superior to four related methods [which include Initial interaction scores method via the neighbors and the Laplacian regularized Least Square (IILLS), Bi-random walk on a heterogeneous network (BRWH), Laplacian regularized least squares classifier (LapRLS), and Collaborative matrix factorization (CMF)]. Furthermore, a case study also demonstrates that NERLS effectively predicts potential virus-receptor interactions.

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.

LDA-LNSUBRW: lncRNA-Disease Association Prediction Based on Linear Neighborhood Similarity and Unbalanced bi-Random Walk.

Increasing number of experiments show that lncRNAs are involved in many biological processes, and their mutations and disorders are associated with many diseases. However, verifying the relationships between lncRNAs and diseases is time consuming and laborio. Searching for effective computational methods will contribute to our understanding of the underlying mechanisms of disease and identifying biomarkers of diseases. Therefore, we proposed a method called lncRNA-disease association prediction based on linear neighborhood similarity and unbalanced bi-random walk (LDA-LNSUBRW). Given that the known lncRNA-disease associations are rare, a pretreatment step should be performed to obtain the interaction possibility of unknown cases, so as to help us predict the potential associations. In the framework of leave-one-out cross-validation (LOOCV)and fivefold cross-validation (5-fold CV), LDA-LNSUBRW achieved effective performance with AUC of 0.8874 and 0.8632 ± 0.0051, respectively. The experimental results in this paper show that the proposed method is superior to five other state-of-the-art methods. In addition, case studies of three diseases (lung cancer, breast cancer, and osteosarcoma)were carried out to illustrate that LDA-LNSUBRW could predict the relevant lncRNAs.

A Novel Drug Repositioning Approach Based on Integrative Multiple Similarity Measures.

Drug repositioning refers to discovering new indications for the existing drugs, which can improve the efficiency of drug research and development. In this work, a novel drug repositioning approach based on integrative multiple similarity measure, called DR_IMSM, is proposed. The process of integrative similarity measure contains three steps. First, a heterogeneous network can be constructed based on known drug-disease association, shared entities information for drug pairwise and diseases pairwise. Second, a deep learning method, DeepWalk, is used to capture the topology similarity for drug and disease. Third, a similarity integration and adjusting process is further conducted to obtain more comprehensive drug and disease similarity measure, respectively. On this basis, a Bi-random walk algorithm is implemented in the constructed heterogeneous network to rank diseases for each drug. Compared with other approaches, the proposed DR_IMSM can achieve superior performance in terms of AUC on the gold standard datasets. Case studies further confirm the practical significance of DR_IMSM.