An increasing number of studies have indicated that long-non-coding RNAs (lncRNAs) play critical roles in many important biological processes. Predicting potential lncRNAdisease associations can improve our understanding of the molecular mechanisms of human diseases and aid in finding biomarkers for disease diagnosis, treatment, and prevention. In this paper, we constructed a bipartite network based on known lncRNA-disease associations; based on this work, we proposed a novel model for inferring potential lncRNA disease associations. Specifically, we analyzed the properties of the bipartite network and found that it closely followed a power-law distribution. Moreover, to evaluate the performance of our model, a leaveone-out cross-validation (LOOCV) framework was implemented, and the simulation results showed that our computational model significantly outperformed previous state-of-the-art models, with AUCs of 0.8825, 0.9004 and 0.9292 for known lncRNAdisease associations obtained from the LncRNADisease database, Lnc2Cancer database, and MNDR database, respectively. Thus, our approach may be an excellent addition to the biomedical research field in the future.

An increasing number of studies have indicated that long-non-coding RNAs (lncRNAs) play crucial roles in biological processes, complex disease diagnoses, prognoses, and treatments. However, experimentally validated associations between lncRNAs and diseases are still very limited. Recently, computational models have been developed to discover potential associations between lncRNAs and diseases by integrating multiple heterogeneous biological data; this has become a hot topic in biological research. In this article, we constructed a global tripartite network by integrating a variety of biological information including miRNA–disease, miRNA–lncRNA, and lncRNA–disease associations and interactions. Then, we constructed a global quadruple network by appending gene–lncRNA interaction, gene–disease association, and gene–miRNA interaction networks to the global tripartite network. Subsequently, based on these two global networks, a novel approach was proposed based on the naïve Bayesian classifier to predict potential lncRNA–disease associations (NBCLDA). Comparing with the state-of-the-art methods, our new method does not entirely rely on known lncRNA–disease associations, and can achieve a reliable performance with effective area under ROC curve (AUCs)in leave-one-out cross validation. Moreover, in order to further estimate the performance of NBCLDA, case studies of colorectal cancer, prostate cancer, and glioma were implemented in this paper, and the simulation results demonstrated that NBCLDA can be an excellent tool for biomedical research in the future.

In recent years, with the rapid development of techniques in bioinformatics and life science, a considerable quantity of biomedical data has been accumulated, based on which researchers have developed various computational approaches to discover potential associations between human microbes, drugs and diseases. This paper provides a comprehensive overview of recent advances in prediction of potential correlations between microbes, drugs and diseases from biological data to computational models. Firstly, we introduced the widely used datasets relevant to the identification of potential relationships between microbes, drugs and diseases in detail. And then, we divided a series of a lot of representative computing models into five major categories including network, matrix factorization, matrix completion, regularization and artificial neural network for in-depth discussion and comparison. Finally, we analysed possible challenges and opportunities in this research area, and at the same time we outlined some suggestions for further improvement of predictive performances as well.

Recently, numerous laboratory studies have indicated that many microRNAs (miRNAs) are involved in and associated with human diseases and can serve as potential biomarkers and drug targets. Therefore, developing effective computational models for the prediction of novel associations between diseases and miRNAs could be beneficial for achieving an understanding of disease mechanisms at the miRNA level and the interactions between diseases and miRNAs at the disease level. Thus far, only a few miRNA-disease association pairs are known, and models analyzing miRNA-disease associations based on lncRNA are limited. In this study, a new computational method based on a distance correlation set is developed to predict miRNA-disease associations (DCSMDA) by integrating known lncRNA-disease associations, known miRNA-lncRNA associations, disease semantic similarity, and various lncRNA and disease similarity measures. The novelty of DCSMDA is due to the construction of a miRNA-lncRNA-disease network, which reveals that DCSMDA can be applied to predict potential lncRNA-disease associations without requiring any known miRNA-disease associations. Although the implementation of DCSMDA does not require known disease-miRNA associations, the area under curve is 0.8155 in the leave-one-out cross validation. Furthermore, DCSMDA was implemented in case studies of prostatic neoplasms, lung neoplasms and leukaemia, and of the top 10 predicted associations, 10, 9 and 9 associations, respectively, were separately verified in other independent studies and biological experimental studies. In addition, 10 of the 10 (100%) associations predicted by DCSMDA were supported by recent bioinformatical studies. According to the simulation results, DCSMDA can be a great addition to the biomedical research field.

Accumulating studies have indicated that the human microbiome plays critical roles in human health and disease. It is significantly important to analyze the relationships between microbe and disease, which is helpful for the understanding of diseases mechanism, diagnosis and therapy. Predicting potential microbe-disease associations would not only boost understanding of the disease mechanism but also it will help to find biomarkers for disease diagnosis and prognosis. However, a systems understanding of microbe-disease relationships is mostly undefined. In this article, we construct a gene-disease bipartite network and a microbe-disease bipartite network based on known human gene-disease associations and microbe-disease interactions, respectively. Then, a model for predicting potential microbe-disease associations is designed based on the two bipartite networks. In order to evaluate the prediction performance of our proposed method, Leave-One-Out Cross Validation (LOOCV) and K-fold Cross Validation procedures are implemented. Simulation results show that our method can achieve a reliable performance with AUC of 0.8993 based on microbe-disease associations downloaded from HMDAD database and human gene-disease relationships downloaded from DisGeNET database. Furthermore, it also can reach to a sound performance with AUC of 0.8640 only based on the known microbe-disease associations in the framework of LOOCV, which is clearly better than the performance of state of the art KATZHMDA model with AUC of 0.8382. It is anticipated that our proposed model could be used to predict more potential microbe-disease associations, which is helpful for the understanding of disease mechanism, diagnosis and prognosis.

In this paper, a novel method to analyze the similarity/dissimilarity of protein sequences based on Principal Component Analysis-Fast Fourier Transformation (PCA-FFT) is proposed, in which, the PCA is utilized to transform protein sequences into time series and the FFT is utilized to analyze the time series while considering them as signals. To test the effectiveness of our newly proposed method, it is applied to analyze the similarity/dissimilarity of 16 different ND5 protein sequences and 29 different spike protein sequences, respectively. Furthermore, the correlation analysis is presented for comparing with others methods, and the simulation results show that it has better performances in the aspects of computation complexity and recognition degree than some existing methods.