An Increase in microbial activity is shown to be intimately connected with the pathogenesis of diseases. Considering the expense of traditional verification methods, researchers are working to develop high-efficiency methods for detecting potential disease-related microbes. In this article, a new prediction method, MSF-LRR, is established, which uses Low-Rank Representation (LRR) to perform multi-similarity information fusion to predict disease-related microbes. Considering that most existing methods only use one class of similarity, three classes of microbe and disease similarity are added. Then, LRR is used to obtain low-rank structural similarity information. Additionally, the method adaptively extracts the local low-rank structure of the data from a global perspective, to make the information used for the prediction more effective. Finally, a neighbor-based prediction method that utilizes the concept of collaborative filtering is applied to predict unknown microbe-disease pairs. As a result, the AUC value of MSF-LRR is superior to other existing algorithms under 5-fold cross-validation. Furthermore, in case studies, excluding originally known associations, 16 and 19 of the top 20 microbes associated with Bacterial Vaginosis and Irritable Bowel Syndrome, respectively, have been confirmed by the recent literature. In summary, MSF-LRR is a good predictor of potential microbe-disease associations and can contribute to drug discovery and biological research.

MicroRNAs (miRNAs) are single-stranded small RNAs. An increasing number of studies have shown that miRNAs play a vital role in many important biological processes. However, some experimental methods to predict unknown miRNA-disease associations (MDAs) are time-consuming and costly. Only a small percentage of MDAs are verified by researchers. Therefore, there is a great need for high-speed and efficient methods to predict novel MDAs. In this paper, a new computational method based on Dual-Network Information Fusion (DNIF) is developed to predict potential MDAs. Specifically, on the one hand, two enhanced sub-models are integrated to reconstruct an effective prediction framework; on the other hand, the prediction performance of the algorithm is improved by fully fusing multiple omics data information, including validated miRNA-disease associations network, miRNA functional similarity, disease semantic similarity and Gaussian interaction profile (GIP) kernel network associations. As a result, DNIF achieves the excellent performance under situation of 5-fold cross validation (average AUC of 0.9571). In the cases study of three important human diseases, our model has achieved satisfactory performance in predicting potential miRNAs for certain diseases. The reliable experimental results demonstrate that DNIF could serve as an effective calculation method to accelerate the identification of MDAs.

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.

Determining microRNA (miRNA)-disease associations (MDAs) is an integral part in the prevention, diagnosis, and treatment of complex diseases. However, wet experiments to discern MDAs are inefficient and expensive. Hence, the development of reliable and efficient data integrative models for predicting MDAs is of significant meaning. In the present work, a novel deep learning method for predicting MDAs through deep autoencoder with multiple kernel learning (DAEMKL) is presented. Above all, DAEMKL applies multiple kernel learning (MKL) in miRNA space and disease space to construct miRNA similarity network and disease similarity network, respectively. Then, for each disease or miRNA, its feature representation is learned from the miRNA similarity network and disease similarity network via the regression model. After that, the integrated miRNA feature representation and disease feature representation are input into deep autoencoder (DAE). Furthermore, the novel MDAs are predicted through reconstruction error. Ultimately, the AUC results show that DAEMKL achieves outstanding performance. In addition, case studies of three complex diseases further prove that DAEMKL has excellent predictive performance and can discover a large number of underlying MDAs. On the whole, our method DAEMKL is an effective method to identify MDAs.

A growing number of clinical studies have provided substantial evidence of a close relationship between the microbe and the disease. Thus, it is necessary to infer potential microbe-disease associations. But traditional approaches use experiments to validate these associations that often spend a lot of materials and time. Hence, more reliable computational methods are expected to be applied to predict disease-associated microbes. In this article, an innovative mean for predicting microbe-disease associations is proposed, which is based on network consistency projection and label propagation (NCPLP). Given that most existing algorithms use the Gaussian interaction profile (GIP) kernel similarity as the similarity criterion between microbe pairs and disease pairs, in this model, Medical Subject Headings descriptors are considered to calculate disease semantic similarity. In addition, 16S rRNA gene sequences are borrowed for the calculation of microbe functional similarity. In view of the gene-based sequence information, we use two conventional methods (BLAST+ and MEGA7) to assess the similarity between each pair of microbes from different perspectives. Especially, network consistency projection is added to obtain network projection scores from the microbe space and the disease space. Ultimately, label propagation is utilized to reliably predict microbes related to diseases. NCPLP achieves better performance in various evaluation indicators and discovers a greater number of potential associations between microbes and diseases. Also, case studies further confirm the reliable prediction performance of NCPLP. To conclude, our algorithm NCPLP has the ability to discover these underlying microbe-disease associations and can provide help for biological study.

BACKGROUND: With the rapid development of various advanced biotechnologies, researchers in related fields have realized that microRNAs (miRNAs) play critical roles in many serious human diseases. However, experimental identification of new miRNA-disease associations (MDAs) is expensive and time-consuming. Practitioners have shown growing interest in methods for predicting potential MDAs. In recent years, an increasing number of computational methods for predicting novel MDAs have been developed, making a huge contribution to the research of human diseases and saving considerable time. In this paper, we proposed an efficient computational method, named bipartite graph-based collaborative matrix factorization (BGCMF), which is highly advantageous for predicting novel MDAs. RESULTS: By combining two improved recommendation methods, a new model for predicting MDAs is generated. Based on the idea that some new miRNAs and diseases do not have any associations, we adopt the bipartite graph based on the collaborative matrix factorization method to complete the prediction. The BGCMF achieves a desirable result, with AUC of up to 0.9514 ± (0.0007) in the five-fold cross-validation experiments. CONCLUSIONS: Five-fold cross-validation is used to evaluate the capabilities of our method. Simulation experiments are implemented to predict new MDAs. More importantly, the AUC value of our method is higher than those of some state-of-the-art methods. Finally, many associations between new miRNAs and new diseases are successfully predicted by performing simulation experiments, indicating that BGCMF is a useful method to predict more potential miRNAs with roles in various diseases.

As is known to all, constructing experiments to predict unknown miRNA-disease association is time-consuming, laborious and costly. Accordingly, new prediction model should be conducted to predict novel miRNA-disease associations. What's more, the performance of this method should be high and reliable. In this paper, a new computation model Logistic Weighted Profile-based Collaborative Matrix Factorization (LWPCMF) is put forward. In this method, weighted profile (WP) is combined with collaborative matrix factorization (CMF) to increase the performance of this model. And, the neighbor information is considered. In addition, logistic function is applied to miRNA functional similarity matrix and disease semantic similarity matrix to extract valuable information. At the same time, by adding WP and logistic function, the known correlation can be protected. And, Gaussian Interaction Profile (GIP) kernels of miRNAs and diseases are added to miRNA functional similarity network and disease semantic similarity network to augment kernel similarities. Then, a five-fold cross validation is implemented to evaluate the predictive ability of this method. Besides, case studies are conducted to view the experimental results. The final result contains not only known associations but also newly predicted ones. And, the result proves that our method is better than other existing methods. This model is able to predict potential miRNA-disease associations.

BACKGROUND: MicroRNAs (miRNAs) are non-coding RNAs with regulatory functions. Many studies have shown that miRNAs are closely associated with human diseases. Among the methods to explore the relationship between the miRNA and the disease, traditional methods are time-consuming and the accuracy needs to be improved. In view of the shortcoming of previous models, a method, collaborative matrix factorization based on matrix completion (MCCMF) is proposed to predict the unknown miRNA-disease associations. RESULTS: The complete matrix of the miRNA and the disease is obtained by matrix completion. Moreover, Gaussian Interaction Profile kernel is added to the miRNA functional similarity matrix and the disease semantic similarity matrix. Then the Weight K Nearest Known Neighbors method is used to pretreat the association matrix, so the model is close to the reality. Finally, collaborative matrix factorization method is applied to obtain the prediction results. Therefore, the MCCMF obtains a satisfactory result in the fivefold cross-validation, with an AUC of 0.9569 (0.0005). CONCLUSIONS: The AUC value of MCCMF is higher than other advanced methods in the fivefold cross validation experiment. In order to comprehensively evaluate the performance of MCCMF, accuracy, precision, recall and f-measure are also added. The final experimental results demonstrate that MCCMF outperforms other methods in predicting miRNA-disease associations. In the end, the effectiveness and practicability of MCCMF are further verified by researching three specific diseases.