Inferring microRNA-disease associations by random walk on a heterogeneous network with multiple data sources

Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities

New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include myriad properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field.

Computational Methods for Identifying Similar Diseases

Although our knowledge of human diseases has increased dramatically, the molecular basis, phenotypic traits, and therapeutic targets of most diseases still remain unclear. An increasing number of studies have observed that similar diseases often are caused by similar molecules, can be diagnosed by similar markers or phenotypes, or can be cured by similar drugs. Thus, the identification of diseases similar to known ones has attracted considerable attention worldwide. To this end, the associations between diseases at the molecular, phenotypic, and taxonomic levels were used to measure the pairwise similarity in diseases. The corresponding performance assessment strategies for these methods involving the terms “category-based,” “simulated-patient-based,” and “benchmark-data-based” were thus further emphasized. Then, frequently used methods were evaluated using a benchmark-data-based strategy. To facilitate the assessment of disease similarity scores, researchers have designed dozens of tools that implement these methods for calculating disease similarity. Currently, disease similarity has been advantageous in predicting noncoding RNA (ncRNA) function and therapeutic drugs for diseases. In this article, we review disease similarity methods, evaluation strategies, tools, and their applications in the biomedical community. We further evaluate the performance of these methods and discuss the current limitations and future trends for calculating disease similarity.

WBNPMD: weighted bipartite network projection for microRNA-disease association prediction

Background

Recently, numerous biological experiments have indicated that microRNAs (miRNAs) play critical roles in exploring the pathogenesis of various human diseases. Since traditional experimental methods for miRNA-disease associations detection are costly and time-consuming, it becomes urgent to design efficient and robust computational techniques for identifying undiscovered interactions.

Methods

In this paper, we proposed a computation framework named weighted bipartite network projection for miRNA-disease association prediction (WBNPMD). In this method, transfer weights were constructed by combining the known miRNA and disease similarities, and the initial information was properly configured. Then the two-step bipartite network algorithm was implemented to infer potential miRNA-disease associations.

Results

The proposed WBNPMD was applied to the known miRNA-disease association data, and leave-one-out cross-validation (LOOCV) and fivefold cross-validation were implemented to evaluate the performance of WBNPMD. As a result, our method achieved the AUCs of 0.9321 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.9173 \pm 0.0005$$\end{document}0.9173±0.0005 in LOOCV and fivefold cross-validation, and outperformed other four state-of-the-art methods. We also carried out two kinds of case studies on prostate neoplasm, colorectal neoplasm, and lung neoplasm, and most of the top 50 predicted miRNAs were confirmed to have an association with the corresponding diseases based on dbDeMC, miR2Disease, and HMDD V3.0 databases.

Conclusions

The experimental results demonstrate that WBNPMD can accurately infer potential miRNA-disease associations. We anticipated that the proposed WBNPMD could serve as a powerful tool for potential miRNA-disease associations excavation.

Electronic supplementary material

The online version of this article (10.1186/s12967-019-2063-4) contains supplementary material, which is available to authorized users.

Predicting miRNA-Disease Associations by Incorporating Projections in Low-Dimensional Space and Local Topological Information

Predicting the potential microRNA (miRNA) candidates associated with a disease helps in exploring the mechanisms of disease development. Most recent approaches have utilized heterogeneous information about miRNAs and diseases, including miRNA similarities, disease similarities, and miRNA-disease associations. However, these methods do not utilize the projections of miRNAs and diseases in a low-dimensional space. Thus, it is necessary to develop a method that can utilize the effective information in the low-dimensional space to predict potential disease-related miRNA candidates. We proposed a method based on non-negative matrix factorization, named DMAPred, to predict potential miRNA-disease associations. DMAPred exploits the similarities and associations of diseases and miRNAs, and it integrates local topological information of the miRNA network. The likelihood that a miRNA is associated with a disease also depends on their projections in low-dimensional space. Therefore, we project miRNAs and diseases into low-dimensional feature space to yield their low-dimensional and dense feature representations. Moreover, the sparse characteristic of miRNA-disease associations was introduced to make our predictive model more credible. DMAPred achieved superior performance for 15 well-characterized diseases with AUCs (area under the receiver operating characteristic curve) ranging from 0.860 to 0.973 and AUPRs (area under the precision-recall curve) ranging from 0.118 to 0.761. In addition, case studies on breast, prostatic, and lung neoplasms demonstrated the ability of DMAPred to discover potential disease-related miRNAs.

Prediction of Disease-related microRNAs through Integrating Attributes of microRNA Nodes and Multiple Kinds of Connecting Edges

Identifying disease-associated microRNAs (disease miRNAs) contributes to the understanding of disease pathogenesis. Most previous computational biology studies focused on multiple kinds of connecting edges of miRNAs and diseases, including miRNA-miRNA similarities, disease-disease similarities, and miRNA-disease associations. Few methods exploited the node attribute information related to miRNA family and cluster. The previous methods do not completely consider the sparsity of node attributes. Additionally, it is challenging to deeply integrate the node attributes of miRNAs and the similarities and associations related to miRNAs and diseases. In the present study, we propose a novel method, known as MDAPred, based on nonnegative matrix factorization to predict candidate disease miRNAs. MDAPred integrates the node attributes of miRNAs and the related similarities and associations of miRNAs and diseases. Since a miRNA is typically subordinate to a family or a cluster, the node attributes of miRNAs are sparse. Similarly, the data for miRNA and disease similarities are sparse. Projecting the miRNA and disease similarities and miRNA node attributes into a common low-dimensional space contributes to estimating miRNA-disease associations. Simultaneously, the possibility that a miRNA is associated with a disease depends on the miRNA's neighbour information. Therefore, MDAPred deeply integrates projections of multiple kinds of connecting edges, projections of miRNAs node attributes, and neighbour information of miRNAs. The cross-validation results showed that MDAPred achieved superior performance compared to other state-of-the-art methods for predicting disease-miRNA associations. MDAPred can also retrieve more actual miRNA-disease associations at the top of prediction results, which is very important for biologists. Additionally, case studies of breast, lung, and pancreatic cancers further confirmed the ability of MDAPred to discover potential miRNA-disease associations.

A Cancer Survival Prediction Method Based on Graph Convolutional Network

BACKGROUND AND OBJECTIVE

Cancer, as the most challenging part in the human disease history, has always been one of the main threats to human life and health. The high mortality of cancer is largely due to the complexity of cancer and the significant differences in clinical outcomes. Therefore, it will be significant to improve accuracy of cancer survival prediction, which has become one of the main fields of cancer research. Many calculation models for cancer survival prediction have been proposed at present, but most of them generate prediction models only by using single genomic data or clinical data. Multiple genomic data and clinical data have not been integrated yet to take a comprehensive consideration of cancers and predict their survival.

METHOD

In order to effectively integrate multiple genomic data (including genetic expression, copy number alteration, DNA methylation and exon expression) and clinical data and apply them to predictive studies on cancer survival, similar network fusion algorithm (SNF) was proposed in this paper to integrate multiple genomic data and clinical data so as to generate sample similarity matrix, min-redundancy and max-relevance algorithm (mRMR) was used to conduct feature selection of multiple genomic data and clinical data of cancer samples and generate sample feature matrix, and finally two matrixes were used for semi-supervised training through graph convolutional network (GCN) so as to obtain a cancer survival prediction method integrating multiple genomic data and clinical data based on graph convolutional network (GCGCN).

RESULT

Performance indexes of GCGCN model indicate that both multiple genomic data and clinical data play significant roles in the accurate survival time prediction of cancer patients. It is compared with existing survival prediction methods, and results show that cancer survival prediction method GCGCN which integrates multiple genomic data and clinical data has obviously superior prediction effect than existing survival prediction methods.

CONCLUSION

All study results in this paper have verified effectiveness and superiority of GCGCN in the aspect of cancer survival prediction.

A rectified factor network based biclustering method for detecting cancer-related coding genes and miRNAs, and their interactions

Detecting cancer-related genes and their interactions is a crucial task in cancer research. For this purpose, we proposed an efficient method, to detect coding genes, microRNAs (miRNAs), and their interactions related to a particular cancer or a cancer subtype using their expression data from the same set of samples. Firstly, biclusters specific to a particular type of cancer are detected based on rectified factor networks and ranked according to their associations with general cancers. Secondly, coding genes and miRNAs in each bicluster are prioritized by considering their differential expression and differential correlation values, protein-protein interaction data, and potential cancer markers. Finally, a rank fusion process is used to obtain the final comprehensive rank by combining multiple ranking results. We applied our proposed method on breast cancer datasets. Results show that our method outperforms other methods in detecting breast cancer-related coding genes and miRNAs. Furthermore, our method is very efficient in computing time, which can handle tens of thousands genes/miRNAs and hundreds of patients in hours on a desktop. This work may aid researchers in studying the genetic architecture of complex diseases, and improving the accuracy of diagnosis.

Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks

Identification of disease-associated miRNAs (disease miRNAs) are critical for understanding etiology and pathogenesis. Most previous methods focus on integrating similarities and associating information contained in heterogeneous miRNA-disease networks. However, these methods establish only shallow prediction models that fail to capture complex relationships among miRNA similarities, disease similarities, and miRNA-disease associations. We propose a prediction method on the basis of network representation learning and convolutional neural networks to predict disease miRNAs, called CNNMDA. CNNMDA deeply integrates the similarity information of miRNAs and diseases, miRNA-disease associations, and representations of miRNAs and diseases in low-dimensional feature space. The new framework based on deep learning was built to learn the original and global representation of a miRNA-disease pair. First, diverse biological premises about miRNAs and diseases were combined to construct the embedding layer in the left part of the framework, from a biological perspective. Second, the various connection edges in the miRNA-disease network, such as similarity and association connections, were dependent on each other. Therefore, it was necessary to learn the low-dimensional representations of the miRNA and disease nodes based on the entire network. The right part of the framework learnt the low-dimensional representation of each miRNA and disease node based on non-negative matrix factorization, and these representations were used to establish the corresponding embedding layer. Finally, the left and right embedding layers went through convolutional modules to deeply learn the complex and non-linear relationships among the similarities and associations between miRNAs and diseases. Experimental results based on cross validation indicated that CNNMDA yields superior performance compared to several state-of-the-art methods. Furthermore, case studies on lung, breast, and pancreatic neoplasms demonstrated the powerful ability of CNNMDA to discover potential disease miRNAs.

Prediction and interpretation of miRNA-disease associations based on miRNA target genes using canonical correlation analysis

BACKGROUND

It has been shown that the deregulation of miRNAs is associated with the development and progression of many human diseases. To reduce time and cost of biological experiments, a number of algorithms have been proposed for predicting miRNA-disease associations. However, the existing methods rarely investigated the cause-and-effect mechanism behind these associations, which hindered further biomedical follow-ups.

RESULTS

In this study, we presented a CCA-based model in which the possible molecular causes of miRNA-disease associations were comprehensively revealed by extracting correlated sets of genes and diseases based on the co-occurrence of miRNAs in target gene profiles and disease profiles. Our method directly suggested the underlying genes involved, which could be used for experimental tests and confirmation. The inference of associated diseases of a new miRNA was made by taking into account the weight vectors of the extracted sets. We extracted 60 pairs of correlated sets from 404 miRNAs with two profiles for 2796 target genes and 362 diseases. The extracted diseases could be considered as possible outcomes of miRNAs regulating the target genes which appeared in the same set, some of which were supported by independent source of information. Furthermore, we tested our method on the 404 miRNAs under the condition of 5-fold cross validations and received an AUC value of 0.84606. Finally, we extensively inferred miRNA-disease associations for 100 new miRNAs and some interesting prediction results were validated by established databases.

CONCLUSIONS

The encouraging results demonstrated that our method could provide a biologically relevant prediction and interpretation of associations between miRNAs and diseases, which were of great usefulness when guiding biological experiments for scientific research.

Self-Weighted Multi-Kernel Multi-Label Learning for Potential miRNA-Disease Association Prediction

Researchers have realized that microRNAs (miRNAs) play significant roles in the pathogenesis of various diseases. Although many computational models have been proposed to predict the associations between miRNAs and diseases, prediction performance could still be improved. In this paper, we propose a novel self-weighted, multi-kernel, multi-label learning (SwMKML) method to predict disease-related miRNAs. SwMKML adaptively learns two optimal kernel matrices for both miRNAs and diseases from multiple kernels constructed from known miRNA-disease associations. Moreover, the miRNA-disease associations predicted from both spaces are updated simultaneously based on a multi-label framework. Compared with four state-of-the-art computational models, SwMKML achieved best results of 95.5%, 93.1%, and 84.1% in global leave-one-out cross-validation, 5-fold cross-validation, and overall prediction accuracy, respectively. A case study conducted on head and neck neoplasms further identified two potential prognostic biomarkers, hsa-mir-125b-1 and hsa-mir-125b-2, for the disease. SwMKML is freely available at Github, and we anticipate that it may become an effective tool for potential miRNA-disease association prediction.

iCircDA-MF: identification of circRNA-disease associations based on matrix factorization

Circular RNAs (circRNAs) are a group of novel discovered non-coding RNAs with closed-loop structure, which play critical roles in various biological processes. Identifying associations between circRNAs and diseases is critical for exploring the complex disease mechanism and facilitating disease-targeted therapy. Although several computational predictors have been proposed, their performance is still limited. In this study, a novel computational method called iCircDA-MF is proposed. Because the circRNA-disease associations with experimental validation are very limited, the potential circRNA-disease associations are calculated based on the circRNA similarity and disease similarity extracted from the disease semantic information and the known associations of circRNA-gene, gene-disease and circRNA-disease. The circRNA-disease interaction profiles are then updated by the neighbour interaction profiles so as to correct the false negative associations. Finally, the matrix factorization is performed on the updated circRNA-disease interaction profiles to predict the circRNA-disease associations. The experimental results on a widely used benchmark dataset showed that iCircDA-MF outperforms other state-of-the-art predictors and can identify new circRNA-disease associations effectively.

Gradient Boosting Decision Tree-Based Method for Predicting Interactions Between Target Genes and Drugs

Determining the target genes that interact with drugs—drug–target interactions—plays an important role in drug discovery. Identification of drug–target interactions through biological experiments is time consuming, laborious, and costly. Therefore, using computational approaches to predict candidate targets is a good way to reduce the cost of wet-lab experiments. However, the known interactions (positive samples) and the unknown interactions (negative samples) display a serious class imbalance, which has an adverse effect on the accuracy of the prediction results. To mitigate the impact of class imbalance and completely exploit the negative samples, we proposed a new method, named DTIGBDT, based on gradient boosting decision trees, for predicting candidate drug–target interactions. We constructed a drug–target heterogeneous network that contains the drug similarities based on the chemical structures of drugs, the target similarities based on target sequences, and the known drug–target interactions. The topological information of the network was captured by random walks to update the similarities between drugs or targets. The paths between drugs and targets could be divided into multiple categories, and the features of each category of paths were extracted. We constructed a prediction model based on gradient boosting decision trees. The model establishes multiple decision trees with the extracted features and obtains the interaction scores between drugs and targets. DTIGBDT is a method of ensemble learning, and it effectively reduces the impact of class imbalance. The experimental results indicate that DTIGBDT outperforms several state-of-the-art methods for drug–target interaction prediction. In addition, case studies on Quetiapine, Clozapine, Olanzapine, Aripiprazole, and Ziprasidone demonstrate the ability of DTIGBDT to discover potential drug–target interactions.

Upregulated expression of MNX1-AS1 long noncoding RNA predicts poor prognosis in gastric cancer

As important regulators of gene expression long noncoding RNAs (lncRNAs) are implicated in various physiological and pathological processes, including cancer. An oncogenic role of MNX1 antisense RNA 1 (MNX1-AS1) lncRNA has been suggested in cervical cancer and glioblastoma. In this study, we investigated the clinicopathological significance and biological function of MNX1-AS1 in gastric cancer (GC). The expression of MNX1-AS1 was analyzed by qRT-PCR in 96 GC and adjacent non-tumor tissues in relation to clinicopathological features and overall survival (OS) of patients, and in five human GC cell lines compared to a normal gastric epithelial cell line. Loss-of-function experiments using small interfering RNA (siRNA) targeting MNX1-AS1 (si-MNX1-AS1) were carried out in AGS and MGC-803 GC cell lines. Cell proliferation (CCK-8 assay), migration (Transwell) and invasion (Transwell Matrigel), and protein expression of proliferating cell nuclear antigen (PCNA), E-cadherin, N-cadherin, vimentin and matrix metallopeptidase 9 (MMP-9) were analyzed in transfected GC cells. Expression of MNX1-AS1 was significantly higher in GC vs. adjacent non-tumor tissues. Higher MNX1-AS1 expression was significantly associated with tumor size, TNM stage and lymph node metastasis. Kaplan-Meier analysis showed that GC patients with higher MNX1-AS1 expression had worse OS compared to patients with lower MNX1-AS1 expression. Multivariate analysis showed that MNX1-AS1 is an independent poor prognostic factor in GC. Knockdown of MNX1-AS1 significantly inhibited proliferation, migration and invasion of AGS and MGC-803 cells, and resulted in increased E-cadherin and decreased PCNA, N-cadherin, vimentin and MMP-9 expression. Taken together, these results suggest that MNX1-AS1 has an oncogenic function in GC and potential as a molecular target in GC therapy.

Bipartite Heterogeneous Network Method Based on Co-neighbor for MiRNA-Disease Association Prediction

In recent years, miRNA variation and dysregulation have been found to be closely related to human tumors, and identifying miRNA-disease associations is helpful for understanding the mechanisms of disease or tumor development and is greatly significant for the prognosis, diagnosis, and treatment of human diseases. This article proposes a Bipartite Heterogeneous network link prediction method based on co-neighbor to predict miRNA-disease association (BHCN). According to the structural characteristics of the bipartite network, the concept of bipartite network co-neighbors is proposed, and the co-neighbors were used to represent the probability of association between disease and miRNA. To predict the isolated diseases and the new miRNA based on the association probability expressed by co-neighbors, we utilized the similarity between disease nodes and the similarity between miRNA nodes in heterogeneous networks to represent the association probability between disease and miRNA. The model's predictive performance was evaluated by the leave-one-out cross validation (LOOCV) on different datasets. The AUC value of BHCN on the gold benchmark dataset was 0.7973, and the AUC obtained on the prediction dataset was 0.9349, which was better than that of the classic global algorithm. In this case study, we conducted predictive studies on breast neoplasms and colon neoplasms. Most of the top 50 predicted results were confirmed by three databases, namely, HMDD, miR2disease, and dbDEMC, with accuracy rates of 96 and 82%. In addition, BHCN can be used for predicting isolated diseases (without any known associated diseases) and new miRNAs (without any known associated miRNAs). In the isolated disease case study, the top 50 of breast neoplasm and colon neoplasm potentials associated with miRNAs predicted an accuracy of 100 and 96%, respectively, thereby demonstrating the favorable predictive power of BHCN for potentially relevant miRNAs.

Application of Machine Learning in Microbiology

Microorganisms are ubiquitous and closely related to people's daily lives. Since they were first discovered in the 19th century, researchers have shown great interest in microorganisms. People studied microorganisms through cultivation, but this method is expensive and time consuming. However, the cultivation method cannot keep a pace with the development of high-throughput sequencing technology. To deal with this problem, machine learning (ML) methods have been widely applied to the field of microbiology. Literature reviews have shown that ML can be used in many aspects of microbiology research, especially classification problems, and for exploring the interaction between microorganisms and the surrounding environment. In this study, we summarize the application of ML in microbiology.

A Positive Causal Influence of IL-18 Levels on the Risk of T2DM: A Mendelian Randomization Study

A large number of clinical studies have shown that interleukin-18 (IL-18) plasma levels are positively correlated with the pathogenesis and development of type 2 diabetes mellitus (T2DM), but it remains unclear whether IL-18 causes T2DM, primarily due to the influence of reverse causality and residual confounding factors. Genome-wide association studies have led to the discovery of numerous common variants associated with IL-18 and T2DM and opened unprecedented opportunities for investigating possible associations between genetic traits and diseases. In this study, we employed a two-sample Mendelian randomization (MR) method to analyze the causal relationships between IL-18 plasma levels and T2DM using IL18-related SNPs as genetic instrumental variables (IVs). We first selected eight SNPs that were significantly associated with IL-18 but independent of T2DM. We then used these SNPs as IVs to evaluate their effects on T2DM using the inverse-variance weighted (IVW) method. Finally, we conducted sensitivity analysis and MR-Egger regression analysis to evaluate the heterogeneity and pleiotropic effects of each variant. The results based on the IVW method demonstrate that high IL-18 plasma levels significantly increase the risk of T2DM, and no heterogeneity or pleiotropic effects appeared after the sensitivity and MR-Egger analyses.

FCMDAP: using miRNA family and cluster information to improve the prediction accuracy of disease related miRNAs

Background

Biological experiments have confirmed the association between miRNAs and various diseases. However, such experiments are costly and time consuming. Computational methods help select potential disease-related miRNAs to improve the efficiency of biological experiments.

Methods

In this work, we develop a novel method using multiple types of data to calculate miRNA and disease similarity based on mutual information, and add miRNA family and cluster information to predict human disease-related miRNAs (FCMDAP). This method not only depends on known miRNA-diseases associations but also accurately measures miRNA and disease similarity and resolves the problem of overestimation. FCMDAP uses the k most similar neighbor recommendation algorithm to predict the association score between miRNA and disease. Information about miRNA cluster is also used to improve prediction accuracy.

Result

FCMDAP achieves an average AUC of 0.9165 based on leave-one-out cross validation. Results confirm the 100, 98 and 96% of the top 50 predicted miRNAs reported in case studies on colorectal, lung, and pancreatic neoplasms. FCMDAP also exhibits satisfactory performance in predicting diseases without any related miRNAs and miRNAs without any related diseases.

Conclusions

In this study, we present a computational method FCMDAP to improve the prediction accuracy of disease related miRNAs. FCMDAP could be an effective tool for further biological experiments.

Electronic supplementary material

The online version of this article (10.1186/s12918-019-0696-9) contains supplementary material, which is available to authorized users.

Adaptive multi-view multi-label learning for identifying disease-associated candidate miRNAs

Increasing evidence has indicated that microRNAs(miRNAs) play vital roles in various pathological processes and thus are closely related with many complex human diseases. The identification of potential disease-related miRNAs offers new opportunities to understand disease etiology and pathogenesis. Although there have been numerous computational methods proposed to predict reliable miRNA-disease associations, they suffer from various limitations that affect the prediction accuracy and their applicability. In this study, we develop a novel method to discover disease-related candidate miRNAs based on Adaptive Multi-View Multi-Label learning(AMVML). Specifically, considering the inherent noise existed in the current dataset, we propose to learn a new affinity graph adaptively for both diseases and miRNAs from multiple similarity profiles. We then simultaneously update the miRNA-disease association predicted from both spaces based on multi-label learning. In particular, we prove the convergence of AMVML theoretically and the corresponding analysis indicates that it has a fast convergence rate. To comprehensively illustrate the prediction performance of our method, we compared AMVML with four state-of-the-art methods under different validation frameworks. As a result, our method achieved comparable performance under various evaluation metrics, which suggests that our method is capable of discovering greater number of true miRNA-disease associations. The case study conducted on thyroid neoplasms further identified a potential diagnostic biomarker. Together, the experimental results confirms the utility of our method and we anticipate that our method could serve as a reliable and efficient tool for uncovering novel disease-related miRNAs.

LMTRDA: Using logistic model tree to predict MiRNA-disease associations by fusing multi-source information of sequences and similarities

Emerging evidence has shown microRNAs (miRNAs) play an important role in human disease research. Identifying potential association among them is significant for the development of pathology, diagnose and therapy. However, only a tiny portion of all miRNA-disease pairs in the current datasets are experimentally validated. This prompts the development of high-precision computational methods to predict real interaction pairs. In this paper, we propose a new model of Logistic Model Tree for predicting miRNA-Disease Association (LMTRDA) by fusing multi-source information including miRNA sequences, miRNA functional similarity, disease semantic similarity, and known miRNA-disease associations. In particular, we introduce miRNA sequence information and extract its features using natural language processing technique for the first time in the miRNA-disease prediction model. In the cross-validation experiment, LMTRDA obtained 90.51% prediction accuracy with 92.55% sensitivity at the AUC of 90.54% on the HMDD V3.0 dataset. To further evaluate the performance of LMTRDA, we compared it with different classifier and feature descriptor models. In addition, we also validate the predictive ability of LMTRDA in human diseases including Breast Neoplasms, Breast Neoplasms and Lymphoma. As a result, 28, 27 and 26 out of the top 30 miRNAs associated with these diseases were verified by experiments in different kinds of case studies. These experimental results demonstrate that LMTRDA is a reliable model for predicting the association among miRNAs and diseases.

Identification of Phage Viral Proteins With Hybrid Sequence Features

The uniqueness of bacteriophages plays an important role in bioinformatics research. In real applications, the function of the bacteriophage virion proteins is the main area of interest. Therefore, it is very important to classify bacteriophage virion proteins and non-phage virion proteins accurately. Extracting comprehensive and effective sequence features from proteins plays a vital role in protein classification. In order to more fully represent protein information, this paper is more comprehensive and effective by combining the features extracted by the feature information representation algorithm based on sequence information (CCPA) and the feature representation algorithm based on sequence and structure information. After extracting features, the Max-Relevance-Max-Distance (MRMD) algorithm is used to select the optimal feature set with the strongest correlation between class labels and low redundancy between features. Given the randomness of the samples selected by the random forest classification algorithm and the randomness features for producing each node variable, a random forest method is employed to perform 10-fold cross-validation on the bacteriophage protein classification. The accuracy of this model is as high as 93.5% in the classification of phage proteins in this study. This study also found that, among the eight physicochemical properties considered, the charge property has the greatest impact on the classification of bacteriophage proteins These results indicate that the model discussed in this paper is an important tool in bacteriophage protein research.

Long Noncoding RNA and Protein Interactions: From Experimental Results to Computational Models Based on Network Methods

Non-coding RNAs with a length of more than 200 nucleotides are long non-coding RNAs (lncRNAs), which have gained tremendous attention in recent decades. Many studies have confirmed that lncRNAs have important influence in post-transcriptional gene regulation; for example, lncRNAs affect the stability and translation of splicing factor proteins. The mutations and malfunctions of lncRNAs are closely related to human disorders. As lncRNAs interact with a variety of proteins, predicting the interaction between lncRNAs and proteins is a significant way to depth exploration functions and enrich annotations of lncRNAs. Experimental approaches for lncRNA–protein interactions are expensive and time-consuming. Computational approaches to predict lncRNA–protein interactions can be grouped into two broad categories. The first category is based on sequence, structural information and physicochemical property. The second category is based on network method through fusing heterogeneous data to construct lncRNA related heterogeneous network. The network-based methods can capture the implicit feature information in the topological structure of related biological heterogeneous networks containing lncRNAs, which is often ignored by sequence-based methods. In this paper, we summarize and discuss the materials, interaction score calculation algorithms, advantages and disadvantages of state-of-the-art algorithms of lncRNA–protein interaction prediction based on network methods to assist researchers in selecting a suitable method for acquiring more dependable results. All the related different network data are also collected and processed in convenience of users, and are available at https://github.com/HAN-Siyu/APINet/.

Diagnosis of Brain Diseases via Multi-Scale Time-Series Model

The functional magnetic resonance imaging (fMRI) data and brain network analysis have been widely applied to automated diagnosis of neural diseases or brain diseases. The fMRI time series data not only contains specific numerical information, but also involves rich dynamic temporal information, those previous graph theory approaches focus on local topology structure and lose contextual information and global fluctuation information. Here, we propose a novel multi-scale functional connectivity for identifying the brain disease via fMRI data. We calculate the discrete probability distribution of co-activity between different brain regions with various intervals. Also, we consider nonsynchronous information under different time dimensions, for analyzing the contextual information in the fMRI data. Therefore, our proposed method can be applied to more disease diagnosis and other fMRI data, particularly automated diagnosis of neural diseases or brain diseases. Finally, we adopt Support Vector Machine (SVM) on our proposed time-series features, which can be applied to do the brain disease classification and even deal with all time-series data. Experimental results verify the effectiveness of our proposed method compared with other outstanding approaches on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and Major Depressive Disorder (MDD) dataset. Therefore, we provide an efficient system via a novel perspective to study brain networks.

The Recent Applications and Developments of Bioinformatics and Omics Technologies in Traditional Chinese Medicine

Background:

Traditional Chinese Medicine (TCM) is widely utilized as complementary health care in China whose acceptance is still hindered by conventional scientific research methodology, although it has been exercised and implemented for nearly 2000 years. Identifying the molecular mechanisms, targets and bioactive components in TCM is a critical step in the modernization of TCM because of the complexity and uniqueness of the TCM system. With recent advances in computational approaches and high throughput technologies, it has become possible to understand the potential TCM mechanisms at the molecular and systematic level, to evaluate the effectiveness and toxicity of TCM treatments. Bioinformatics is gaining considerable attention to unearth the in-depth molecular mechanisms of TCM, which emerges as an interdisciplinary approach owing to the explosive omics data and development of computer science. Systems biology, based on the omics techniques, opens up a new perspective which enables us to investigate the holistic modulation effect on the body.

Objective:

This review aims to sum up the recent efforts of bioinformatics and omics techniques in the research of TCM including Systems biology, Metabolomics, Proteomics, Genomics and Transcriptomics.

Conclusion:

Overall, bioinformatics tools combined with omics techniques have been extensively used to scientifically support the ancient practice of TCM to be scientific and international through the acquisition, storage and analysis of biomedical data.

A review on machine learning principles for multi-view biological data integration

Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.

Inferring microRNA-disease association by hybrid recommendation algorithm and unbalanced bi-random walk on heterogeneous network

More and more research works have indicated that microRNAs (miRNAs) play indispensable roles in exploring the pathogenesis of diseases. Detecting miRNA-disease associations by experimental techniques in biology is expensive and time-consuming. Hence, it is important to propose reliable and accurate computational methods to exploring potential miRNAs related diseases. In our work, we develop a novel method (BRWHNHA) to uncover potential miRNAs associated with diseases based on hybrid recommendation algorithm and unbalanced bi-random walk. We first integrate the Gaussian interaction profile kernel similarity into the miRNA functional similarity network and the disease semantic similarity network. Then we calculate the transition probability matrix of bipartite network by using hybrid recommendation algorithm. Finally, we adopt unbalanced bi-random walk on the heterogeneous network to infer undiscovered miRNA-disease relationships. We tested BRWHNHA on 22 diseases based on five-fold cross-validation and achieves reliable performance with average AUC of 0.857, which an area under the ROC curve ranging from 0.807 to 0.924. As a result, BRWHNHA significantly improves the performance of inferring potential miRNA-disease association compared with previous methods. Moreover, the case studies on lung neoplasms and prostate neoplasms also illustrate that BRWHNHA is superior to previous prediction methods and is more advantageous in exploring potential miRNAs related diseases. All source codes can be downloaded from https://github.com/myl446/BRWHNHA.

Exposing the Causal Effect of Body Mass Index on the Risk of Type 2 Diabetes Mellitus: A Mendelian Randomization Study

Introduction: High body mass index (BMI) is a positive associated phenotype of type 2 diabetes mellitus (T2DM). Abundant studies have observed this from a clinical perspective. Since the rapid increase in a large number of genetic variants from the genome-wide association studies (GWAS), common SNPs of BMI and T2DM were identified as the genetic basis for understanding their associations. Currently, their causality is beginning to blur. Materials and Methods: To classify it, a Mendelian randomisation (MR), using genetic instrumental variables (IVs) to explore the causality of intermediate phenotype and disease, was utilized here to test the effect of BMI on the risk of T2DM. In this article, MR was carried out on GWAS data using 52 independent BMI SNPs as IVs. The pooled odds ratio (OR) of these SNPs was calculated using inverse-variance weighted method for the assessment of 5 kg/m2 higher BMI on the risk of T2DM. The leave-one-out validation was conducted to identify the effect of individual SNPs. MR-Egger regression was utilized to detect potential pleiotropic bias of variants. Results: We obtained the high OR (1.470; 95% CI 1.170 to 1.847; P = 0.001), low intercept (0.004, P = 0.661), and small fluctuation of ORs {from -0.039 [(1.412 - 1.470) / 1.470)] to 0.075 [(1.568- 1.470) / 1.470)] in leave-one-out validation. Conclusion: We validate the causal effect of high BMI on the risk of T2DM. The low intercept shows no pleiotropic bias of IVs. The small alterations of ORs activated by removing individual SNPs showed no single SNP drives our estimate.

Molecular Network-Based Drug Prediction in Thyroid Cancer

As a common malignant tumor disease, thyroid cancer lacks effective preventive and therapeutic drugs. Thus, it is crucial to provide an effective drug selection method for thyroid cancer patients. The connectivity map (CMAP) project provides an experimental validated strategy to repurpose and optimize cancer drugs, the rationale behind which is to select drugs to reverse the gene expression variations induced by cancer. However, it has a few limitations. Firstly, CMAP was performed on cell lines, which are usually different from human tissues. Secondly, only gene expression information was considered, while the information about gene regulations and modules/pathways was more or less ignored. In this study, we first measured comprehensively the perturbations of thyroid cancer on a patient including variations at gene expression level, gene co-expression level and gene module level. After that, we provided a drug selection pipeline to reverse the perturbations based on drug signatures derived from tissue studies. We applied the analyses pipeline to the cancer genome atlas (TCGA) thyroid cancer data consisting of 56 normal and 500 cancer samples. As a result, we obtained 812 up-regulated and 213 down-regulated genes, whose functions are significantly enriched in extracellular matrix and receptor localization to synapses. In addition, a total of 33,778 significant differentiated co-expressed gene pairs were found, which form a larger module associated with impaired immune function and low immunity. Finally, we predicted drugs and gene perturbations that could reverse the gene expression and co-expression changes incurred by the development of thyroid cancer through the Fisher’s exact test. Top predicted drugs included validated drugs like baclofen, nevirapine, glucocorticoid, formaldehyde and so on. Combining our analyses with literature mining, we inferred that the regulation of thyroid hormone secretion might be closely related to the inhibition of the proliferation of thyroid cancer cells.

FMSM: a novel computational model for predicting potential miRNA biomarkers for various human diseases

Background

MicroRNA (miRNA) plays a key role in regulation mechanism of human biological processes, including the development of disease and disorder. It is necessary to identify potential miRNA biomarkers for various human diseases. Computational prediction model is expected to accelerate the process of identification.

Results

Considering the limitations of previously proposed models, we present a novel computational model called FMSM. It infers latent miRNA biomarkers involved in the mechanism of various diseases based on the known miRNA-disease association network, miRNA expression similarity, disease semantic similarity and Gaussian interaction profile kernel similarity. FMSM achieves reliable prediction performance in 5-fold and leave-one-out cross validations with area under ROC curve (AUC) values of 0.9629+/− 0.0127 and 0.9433, respectively, which outperforms the state-of-the-art competitors and classical algorithms. In addition, 19 of top 25 predicted miRNAs have been validated to have associations with Colonic Neoplasms in case study.

Conclusions

A factored miRNA similarity based model and miRNA expression similarity substantially contribute to the well-performing prediction. The list of the predicted most latent miRNA biomarkers of various human diseases is publicized. It is anticipated that FMSM could serve as a useful tool guiding the future experimental validation for those promising miRNA biomarker candidates.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0664-9) contains supplementary material, which is available to authorized users.

FKL-Spa-LapRLS: an accurate method for identifying human microRNA-disease association

BACKGROUND

In the process of post-transcription, microRNAs (miRNAs) are closely related to various complex human diseases. Traditional verification methods for miRNA-disease associations take a lot of time and expense, so it is especially important to design computational methods for detecting potential associations. Considering the restrictions of previous computational methods for predicting potential miRNAs-disease associations, we develop the model of FKL-Spa-LapRLS (Fast Kernel Learning Sparse kernel Laplacian Regularized Least Squares) to break through the limitations.

RESULT

First, we extract three miRNA similarity kernels and three disease similarity kernels. Then, we combine these kernels into a single kernel through the Fast Kernel Learning (FKL) model, and use sparse kernel (Spa) to eliminate noise in the integrated similarity kernel. Finally, we find the associations via Laplacian Regularized Least Squares (LapRLS). Based on three evaluation methods, global and local leave-one-out cross validation (LOOCV), and 5-fold cross validation, the AUCs of our method achieve 0.9563, 0.8398 and 0.9535, thus it can be seen that our method is reliable. Then, we use case studies of eight neoplasms to further analyze the performance of our method. We find that most of the predicted miRNA-disease associations are confirmed by previous traditional experiments, and some important miRNAs should be paid more attention, which uncover more associations of various neoplasms than other miRNAs.

CONCLUSIONS

Our proposed model can reveal miRNA-disease associations and improve the accuracy of correlation prediction for various diseases. Our method can be also easily extended with more similarity kernels.

Integrating Multiple Interaction Networks for Gene Function Inference

In the past few decades, the number and variety of genomic and proteomic data available have increased dramatically. Molecular or functional interaction networks are usually constructed according to high-throughput data and the topological structure of these interaction networks provide a wealth of information for inferring the function of genes or proteins. It is a widely used way to mine functional information of genes or proteins by analyzing the association networks. However, it remains still an urgent but unresolved challenge how to combine multiple heterogeneous networks to achieve more accurate predictions. In this paper, we present a method named ReprsentConcat to improve function inference by integrating multiple interaction networks. The low-dimensional representation of each node in each network is extracted, then these representations from multiple networks are concatenated and fed to gcForest, which augment feature vectors by cascading and automatically determines the number of cascade levels. We experimentally compare ReprsentConcat with a state-of-the-art method, showing that it achieves competitive results on the datasets of yeast and human. Moreover, it is robust to the hyperparameters including the number of dimensions.

A novel semi-supervised model for miRNA-disease association prediction based on [Formula: see text]-norm graph

BACKGROUND

Identification of miRNA-disease associations has attracted much attention recently due to the functional roles of miRNAs implicated in various biological and pathological processes. Great efforts have been made to discover the potential associations between miRNAs and diseases both experimentally and computationally. Although reliable, the experimental methods are in general time-consuming and labor-intensive. In comparison, computational methods are more efficient and applicable to large-scale datasets.

METHODS

In this paper, we propose a novel semi-supervised model to predict miRNA-disease associations via [Formula: see text]-norm graph. Specifically, we first recalculate the miRNA functional similarities as well as the disease semantic similarities based on the latest version of MeSH descriptors and HMDD. We then update the similarity matrices and association matrix iteratively in both miRNA space and disease space. The optimized association matrices from each space are combined together as the final output.

RESULTS

Compared with four state-of-the-art prediction methods, our method achieved favorable performance with AUCs of 0.943 and 0.946 in both global LOOCV and local LOOCV, respectively. In addition, we carried out three types of case studies on five common human diseases, and most of the top 50 predicted miRNAs were confirmed to be associated with the investigated diseases by four databases dbDEMC, PheomiR, miR2Disease and miRwayDB. Specifically, our results provided potential evidence that miRNAs within the same family or cluster were likely to play functional roles together in given diseases.

CONCLUSIONS

Taken together, the experimental results clearly demonstrated the utility of the proposed method. We anticipated that our method could serve as a reliable and efficient tool for miRNA-disease association prediction.

MDA-SKF: Similarity Kernel Fusion for Accurately Discovering miRNA-Disease Association

Identifying accurate associations between miRNAs and diseases is beneficial for diagnosis and treatment of human diseases. It is especially important to develop an efficient method to detect the association between miRNA and disease. Traditional experimental method has high precision, but its process is complicated and time-consuming. Various computational methods have been developed to uncover potential associations based on an assumption that similar miRNAs are always related to similar diseases. In this paper, we propose an accurate method, MDA-SKF, to uncover potential miRNA-disease associations. We first extract three miRNA similarity kernels (miRNA functional similarity, miRNA sequence similarity, Hamming profile similarity for miRNA) and three disease similarity kernels (disease semantic similarity, disease functional similarity, Hamming profile similarity for disease) in two subspaces, respectively. Then, due to limitations that some initial information may be lost in the process and some noises may be exist in integrated similarity kernel, we propose a novel Similarity Kernel Fusion (SKF) method to integrate multiple similarity kernels. Finally, we utilize the Laplacian Regularized Least Squares (LapRLS) method on the integrated kernel to find potential associations. MDA-SKF is evaluated by three evaluation methods, including global leave-one-out cross validation (LOOCV) and local LOOCV and 5-fold cross validation (CV), and achieves AUCs of 0.9576, 0.8356, and 0.9557, respectively. Compared with existing seven methods, MDA-SKF has outstanding performance on global LOOCV and 5-fold. We also test case studies to further analyze the performance of MDA-SKF on 32 diseases. Furthermore, 3200 candidate associations are obtained and a majority of them can be confirmed. It demonstrates that MDA-SKF is an accurate and efficient computational tool for guiding traditional experiments.

High-Throughput Sequencing Strategy for miR-146b-regulated circRNA Expression in Hepatic Stellate Cells

BACKGROUND This study was designed to detect and analyze miR-146b-mediated circular RNA (circRNA) expression in hepatic stellate cells. MATERIAL AND METHODS The experiment was divided into a control group and a siRNA-miR-146b group. The interference efficiency of siRNA-miR-146b was confirmed by real-time quantitative reverse transcription PCR (qRT-PCR) and the cells were collected, and total RNA was collected for high flux sequencing. The miRNA-targeted carcass were predicted. Finally, the expression of 5 circRNAs was verified by qRT-PCR. RESULTS miR-146b expression in the siRNA-miR-146b group was significantly lower than that in the control group. The quality of the original sequencing data and the processed data satisfied with the analysis, and the expression of circRNAs was modulated after the reduction of miR-146b. Among them, 18 circRNAs were upregulated, while 77 circRNAs were downregulated in the miR-146b group compared with the control group. The gene prediction showed that hsa_circ1887 was the largest contact point in miRNA and circRNA regulatory networks. qRT-PCR showed that rno-circRNA-469, rno-circRNA-1138, rno-circRNA-2168 and rno-circRAN-1907 were significantly reduced, while circRNA-1984 was significantly promoted in the siRNA-miR-146b group compared with the control group, which were consistent with the measurements by high-throughput sequencing technique. CONCLUSIONS miR-146b could regulate the expression of circRNAs in HSCs, which might take part in the formation and development of hepatic fibrosis.

An Efficient Classifier for Alzheimer's Disease Genes Identification

Alzheimer’s disease (AD) is considered to one of 10 key diseases leading to death in humans. AD is considered the main cause of brain degeneration, and will lead to dementia. It is beneficial for affected patients to be diagnosed with the disease at an early stage so that efforts to manage the patient can begin as soon as possible. Most existing protocols diagnose AD by way of magnetic resonance imaging (MRI). However, because the size of the images produced is large, existing techniques that employ MRI technology are expensive and time-consuming to perform. With this in mind, in the current study, AD is predicted instead by the use of a support vector machine (SVM) method based on gene-coding protein sequence information. In our proposed method, the frequency of two consecutive amino acids is used to describe the sequence information. The accuracy of the proposed method for identifying AD is 85.7%, which is demonstrated by the obtained experimental results. The experimental results also show that the sequence information of gene-coding proteins can be used to predict AD.

LLCMDA: A Novel Method for Predicting miRNA Gene and Disease Relationship Based on Locality-Constrained Linear Coding

MiRNAs are small non-coding regulatory RNAs which are associated with multiple diseases. Increasing evidence has shown that miRNAs play important roles in various biological and physiological processes. Therefore, the identification of potential miRNA-disease associations could provide new clues to understanding the mechanism of pathogenesis. Although many traditional methods have been successfully applied to discover part of the associations, they are in general time-consuming and expensive. Consequently, computational-based methods are urgently needed to predict the potential miRNA-disease associations in a more efficient and resources-saving way. In this paper, we propose a novel method to predict miRNA-disease associations based on Locality-constrained Linear Coding (LLC). Specifically, we first reconstruct similarity networks for both miRNAs and diseases using LLC and then apply label propagation on the similarity networks to get relevant scores. To comprehensively verify the performance of the proposed method, we compare our method with several state-of-the-art methods under different evaluation metrics. Moreover, two types of case studies conducted on two common diseases further demonstrate the validity and utility of our method. Extensive experimental results indicate that our method can effectively predict potential associations between miRNAs and diseases.

Dual Convolutional Neural Network Based Method for Predicting Disease-Related miRNAs

Identification of disease-related microRNAs (disease miRNAs) is helpful for understanding and exploring the etiology and pathogenesis of diseases. Most of recent methods predict disease miRNAs by integrating the similarities and associations of miRNAs and diseases. However, these methods fail to learn the deep features of the miRNA similarities, the disease similarities, and the miRNA–disease associations. We propose a dual convolutional neural network-based method for predicting candidate disease miRNAs and refer to it as CNNDMP. CNNDMP not only exploits the similarities and associations of miRNAs and diseases, but also captures the topology structures of the miRNA and disease networks. An embedding layer is constructed by combining the biological premises about the miRNA–disease associations. A new framework based on the dual convolutional neural network is presented for extracting the deep feature representation of associations. The left part of the framework focuses on integrating the original similarities and associations of miRNAs and diseases. The novel miRNA and disease similarities which contain the topology structures are obtained by random walks on the miRNA and disease networks, and their deep features are learned by the right part of the framework. CNNDMP achieves the superior prediction performance than several state-of-the-art methods during the cross-validation process. Case studies on breast cancer, colorectal cancer and lung cancer further demonstrate CNNDMP’s powerful ability of discovering potential disease miRNAs.

PWCDA: Path Weighted Method for Predicting circRNA-Disease Associations

CircRNAs have particular biological structure and have proven to play important roles in diseases. It is time-consuming and costly to identify circRNA-disease associations by biological experiments. Therefore, it is appealing to develop computational methods for predicting circRNA-disease associations. In this study, we propose a new computational path weighted method for predicting circRNA-disease associations. Firstly, we calculate the functional similarity scores of diseases based on disease-related gene annotations and the semantic similarity scores of circRNAs based on circRNA-related gene ontology, respectively. To address missing similarity scores of diseases and circRNAs, we calculate the Gaussian Interaction Profile (GIP) kernel similarity scores for diseases and circRNAs, respectively, based on the circRNA-disease associations downloaded from circR2Disease database (http://bioinfo.snnu.edu.cn/CircR2Disease/). Then, we integrate disease functional similarity scores and circRNA semantic similarity scores with their related GIP kernel similarity scores to construct a heterogeneous network made up of three sub-networks: disease similarity network, circRNA similarity network and circRNA-disease association network. Finally, we compute an association score for each circRNA-disease pair based on paths connecting them in the heterogeneous network to determine whether this circRNA-disease pair is associated. We adopt leave one out cross validation (LOOCV) and five-fold cross validations to evaluate the performance of our proposed method. In addition, three common diseases, Breast Cancer, Gastric Cancer and Colorectal Cancer, are used for case studies. Experimental results illustrate the reliability and usefulness of our computational method in terms of different validation measures, which indicates PWCDA can effectively predict potential circRNA-disease associations.

Identification of key differentially expressed MicroRNAs in cancer patients through pan-cancer analysis

microRNAs (miRNAs) functioning in gene silencing have been associated with cancer progression. However, common abnormal miRNA expression patterns and their potential roles in cancer have not yet been evaluated. To account for individual differences between patients, we retrieved miRNA sequencing data for 575 patients with both tumor and adjacent non-tumorous tissues from 14 cancer types from The Cancer Genome Atlas (TCGA). We then performed differential expression analysis using DESeq2 and edgeR. Results showed that cancer types can be grouped based on the distribution of miRNAs with different expression patterns between tumor and non-tumor samples. We found 81 significantly differentially expressed miRNAs (SDEmiRNAs) in a single cancer. We also found 21 key SDEmiRNAs (nine over-expressed and 12 under-expressed) associated with at least eight cancers each and enriched in more than 60% of patients per cancer, including four newly identified SDEmiRNAs (hsa-mir-4746, hsa-mir-3648, hsa-mir-3687, and hsa-mir-1269a). The downstream effects of these 21 SDEmiRNAs on cellular function were evaluated through enrichment and pathway analysis of 7186 protein-coding gene targets mined from literature reports of differential expression of miRNAs in cancer. This analysis enables identification of SDEmiRNA functional similarity in cell proliferation control across a wide range of cancers, and assembly of common regulatory networks over cancer-related pathways. These findings were validated by construction of a regulatory network in the PI3K pathway. This study provides evidence for the value of further analysis of SDEmiRNAs as potential biomarkers and therapeutic targets for cancer diagnosis and treatment.

Application of Molecular Methods in the Identification of Ingredients in Chinese Herbal Medicines

There are several kinds of Chinese herbal medicines originating from diverse sources. However, the rapid taxonomic identification of large quantities of Chinese herbal medicines is difficult using traditional methods, and the process of identification itself is prone to error. Therefore, the traditional methods of Chinese herbal medicine identification must meet higher standards of accuracy. With the rapid development of bioinformatics, methods relying on bioinformatics strategies offer advantages with respect to the speed and accuracy of the identification of Chinese herbal medicine ingredients. This article reviews the applicability and limitations of biochip and DNA barcoding technology in the identification of Chinese herbal medicines. Furthermore, the future development of the two technologies of interest is discussed.

Co-Occurrence Network of High-Frequency Words in the Bioinformatics Literature: Structural Characteristics and Evolution

The subjects of literature are the direct expression of the author’s research results. Mining valuable knowledge helps to save time for the readers to understand the content and direction of the literature quickly. Therefore, the co-occurrence network of high-frequency words in the bioinformatics literature and its structural characteristics and evolution were analysed in this paper. First, 242,891 articles from 47 top bioinformatics periodicals were chosen as the object of the study. Second, the co-occurrence relationship among high-frequency words of these articles was analysed by word segmentation and high-frequency word selection. Then, a co-occurrence network of high-frequency words in bioinformatics literature was built. Finally, the conclusions were drawn by analysing its structural characteristics and evolution. The results showed that the co-occurrence network of high-frequency words in the bioinformatics literature was a small-world network with scale-free distribution, rich-club phenomenon and disassortative matching characteristics. At the same time, the high-frequency words used by authors changed little in 2–3 years but varied greatly in four years because of the influence of the state-of-the-art technology.

Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network

Background

Identifying the interactions between proteins and long non-coding RNAs (lncRNAs) is of great importance to decipher the functional mechanisms of lncRNAs. However, current experimental techniques for detection of lncRNA-protein interactions are limited and inefficient. Many methods have been proposed to predict protein-lncRNA interactions, but few studies make use of the topological information of heterogenous biological networks associated with the lncRNAs.

Results

In this work, we propose a novel approach, PLIPCOM, using two groups of network features to detect protein-lncRNA interactions. In particular, diffusion features and HeteSim features are extracted from protein-lncRNA heterogenous network, and then combined to build the prediction model using the Gradient Tree Boosting (GTB) algorithm. Our study highlights that the topological features of the heterogeneous network are crucial for predicting protein-lncRNA interactions. The cross-validation experiments on the benchmark dataset show that PLIPCOM method substantially outperformed previous state-of-the-art approaches in predicting protein-lncRNA interactions. We also prove the robustness of the proposed method on three unbalanced data sets. Moreover, our case studies demonstrate that our method is effective and reliable in predicting the interactions between lncRNAs and proteins.

Availability

The source code and supporting files are publicly available at: http://denglab.org/PLIPCOM/.

Inferring disease-associated microRNAs in heterogeneous networks with node attributes

Identification of disease-associated microRNAs (disease miRNAs) is an essential step towards discovering causal miRNAs and understanding disease pathogenesis. Two sources of information can be exploited for predicting disease miRNAs: one includes the connections between miRNAs, between diseases, and between miRNAs and diseases, and the other has the attributes of miRNA nodes. The former contains information of miRNA similarities, disease similarities, and miRNA-disease associations. The latter includes the information of the families and clusters that miRNAs belong to. Similar diseases are usually associated with miRNAs that have similar functions and common attributes. However, most of the existing methods for disease miRNA prediction focus only on the connections of miRNAs and diseases. It remains challenging to adequately integrate the connections and miRNA node attributes to identify more reliable candidate disease miRNAs. We propose a non-negative matrix factorization based method, FamCluRank, for predicting disease miRNAs in heterogeneous networks with node attributes. One of the novelties of FamCluRank is to fully utilize these two oversighted characteristics of miRNAs and focuses particularly on a deep integration of miRNA families and cluster attributes. In particular, the integration was achieved by three different means. We first constructed a miRNA-disease heterogeneous network with node attributes where the miRNA nodes have their family and cluster attributes. Second, miRNAs sharing more common families and clusters are more likely to be associated with the diseases that are also related to these families and clusters. On the basis of the biological premise, we constructed a novel prediction model of FamCluRank to deeply integrate the family and cluster attributes of miRNAs. Third, two similar diseases tend to be associated with more common miRNA families and clusters, and vice versa. Hence FamCluRank's prediction model is constructed by concerning not only the possible associations between miRNAs and diseases but also the possible disease-family and disease-cluster associations. Comparison with the state-of-the-art methods showed FamCluRank's superior performance not only on the well-characterized diseases but also on the new ones. Case studies on colorectal neoplasms, pancreatic neoplasms, lung neoplasms, and 32 new diseases demonstrated its ability for discovering potential disease miRNAs. FamCluRank is a potent prioritization tool for screening the reliable candidates for subsequent studies concerning their involvement in the pathogenesis of diseases. The web service of FamCluRank, the candidate disease miRNAs for 329 diseases, and the dataset used to develop FamCluRank are available at http://www.famclurank.top.

GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods

Recently, increasing studies have shown that miRNAs are involved in the development and progression of various complex diseases. Consequently, predicting potential miRNA-disease associations makes an important contribution to understanding the pathogenesis of diseases, developing new drugs as well as designing individualized diagnostic and therapeutic approaches for different human diseases. Nonetheless, the inherent noise and incompleteness in the existing biological datasets have limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA-disease association prediction based on global linear neighborhoods (GLNMDA). Specifically, our method obtains a new miRNA/disease similarity matrix by linearly reconstructing each miRNA/disease according to the known experimentally verified miRNA-disease associations. We then adopt label propagation to infer the potential associations between miRNAs and diseases. As a result, GLNMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.867 and 0.929, respectively) and 5-fold cross validation (average AUC of 0.926). Case studies on five common human diseases further confirmed the utility of our method in discovering latent miRNA-disease pairs. Taken together, GLNMDA could serve as a reliable computational tool for miRNA-disease association prediction.

Identifying and Exploiting Potential miRNA-Disease Associations With Neighborhood Regularized Logistic Matrix Factorization

With the rapid development of biological research, microRNAs (miRNA) have become an attractive topic because lots of experimental studies have revealed the significant associations between miRNAs and diseases. However, considering that experiments are expensive and time-consuming, computational methods for predicting associations between miRNAs and diseases have become increasingly crucial. In this study, we proposed a neighborhood regularized logistic matrix factorization method for miRNA-disease association prediction (NRLMFMDA) by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimentally validation of disease-miRNA association. We used Gaussian interaction profile kernel similarity to cover the shortage of the traditional similarity to make it more reasonable and complete. Furthermore, NRLMFMDA also considered the important influences of the neighborhood information and took full advantage of them to improve the accuracy of the miRNA-disease association prediction. We also improved the accuracy by giving higher weights to the known association data in the process of calculating the potential association probabilities. In the global and the local leave-one-out cross validation, NRLMFMDA got the AUCs of 0.9068 and 0.8239, respectively. Moreover, the average AUC of NRLMFMDA in 5-fold cross validation was 0.8976 ± 0.0034. All the three kinds of cross validations have shown significant advantages to a number of previous models. In the case studies of breast neoplasms, esophageal neoplasms and lymphoma according to known miRNA-disease associations in the recent version of HMDD database, there were 78, 80, and 74% of top 50 predicted related miRNAs verified to have associations with these three diseases, respectively. In the further case studies for new disease without any known related miRNAs and the previous version of HMDD database, there were also high proportions of the predicted miRNAs verified by experimental reports. All the validation experiment results have demonstrated the effectiveness and practicability of NRLFMDA to predict the potential miRNA-disease associations.