MultiSourcDSim: an integrated approach for exploring disease similarity

An Integrative Disease Information Network Approach to Similar Disease Detection

Disease similarity analysis impacts significantly in pathogenesis revealing, treatment recommending, and disease-causing genes predicting. Previous works study the disease similarity based on the semantics obtaining from biomedical ontologies (e.g., disease ontology) or the function of disease-causing molecules. However, such methods almost focus on a single perspective for obtaining disease features, which may lead to biased results for similar disease detection. To address this issue, we propose a disease information network-based integrative approach named MISSION for detecting similar diseases. By leveraging the associations between diseases and other biomedical entities, the disease information network is established first. Then, the disease similarity features extracted from the aspects of disease taxonomy, attributes, literature, and annotations are integrated into the disease information network. Finally, the top-k similar disease query is performed based on the integrative disease information. The experiments conducted on real-world datasets demonstrate that MISSION is effective and useful in similar disease detection.

Metabolite-disease interaction prediction based on logistic matrix factorization and local neighborhood constraints

Background

Increasing evidence indicates that metabolites are closely related to human diseases. Identifying disease-related metabolites is especially important for the diagnosis and treatment of disease. Previous works have mainly focused on the global topological information of metabolite and disease similarity networks. However, the local tiny structure of metabolites and diseases may have been ignored, leading to insufficiency and inaccuracy in the latent metabolite-disease interaction mining.

Methods

To solve the aforementioned problem, we propose a novel metabolite-disease interaction prediction method with logical matrix factorization and local nearest neighbor constraints (LMFLNC). First, the algorithm constructs metabolite-metabolite and disease-disease similarity networks by integrating multi-source heterogeneous microbiome data. Then, the local spectral matrices based on these two networks are established and used as the input of the model, together with the known metabolite-disease interaction network. Finally, the probability of metabolite-disease interaction is calculated according to the learned latent representations of metabolites and diseases.

Results

Extensive experiments on the metabolite-disease interaction data were conducted. The results show that the proposed LMFLNC method outperformed the second-best algorithm by 5.28 and 5.61% in the AUPR and F1, respectively. The LMFLNC method also exhibited several potential metabolite-disease interactions, such as "Cortisol" (HMDB0000063), relating to "21-Hydroxylase deficiency," and "3-Hydroxybutyric acid" (HMDB0000011) and "Acetoacetic acid" (HMDB0000060), both relating to "3-Hydroxy-3-methylglutaryl-CoA lyase deficiency."

Conclusion

The proposed LMFLNC method can well preserve the geometrical structure of original data and can thus effectively predict the underlying associations between metabolites and diseases. The experimental results show its effectiveness in metabolite-disease interaction prediction.

Enhanced disease-disease association with information enriched disease representation

OBJECTIVE

Quantification of disease-disease association (DDA) enables the understanding of disease relationships for discovering disease progression and finding comorbidity. For effective DDA strength calculation, there is a need to address the main challenge of integration of various biomedical aspects of DDA is to obtain an information rich disease representation.

MATERIALS AND METHODS

An enhanced and integrated DDA framework is developed that integrates enriched literature-based with concept-based DDA representation. The literature component of the proposed framework uses PubMed abstracts and consists of improved neural network model that classifies DDAs for an enhanced literature-based DDA representation. Similarly, an ontology-based joint multi-source association embedding model is proposed in the ontology component using Disease Ontology (DO), UMLS, claims insurance, clinical notes etc. Results and Discussion: The obtained information rich disease representation is evaluated on different aspects of DDA datasets such as Gene, Variant, Gene Ontology (GO) and a human rated benchmark dataset. The DDA scores calculated using the proposed method achieved a high correlation mainly in gene-based dataset. The quantified scores also shown better correlation of 0.821, when evaluated on human rated 213 disease pairs. In addition, the generated disease representation is proved to have substantial effect on correlation of DDA scores for different categories of disease pairs.

CONCLUSION

The enhanced context and semantic DDA framework provides an enriched disease representation, resulting in high correlated results with different DDA datasets. We have also presented the biological interpretation of disease pairs. The developed framework can also be used for deriving the strength of other biomedical associations.

Exploring novel disease-disease associations based on multi-view fusion network

Established taxonomy system based on disease symptom and tissue characteristics have provided an important basis for physicians to correctly identify diseases and treat them successfully. However, these classifications tend to be based on phenotypic observations, lacking a molecular biological foundation. Therefore, there is an urgent to integrate multi-dimensional molecular biological information or multi-omics data to redefine disease classification in order to provide a powerful perspective for understanding the molecular structure of diseases. Therefore, we offer a flexible disease classification that integrates the biological process, gene expression, and symptom phenotype of diseases, and propose a disease-disease association network based on multi-view fusion. We applied the fusion approach to 223 diseases and divided them into 24 disease clusters. The contribution of internal and external edges of disease clusters were analyzed. The results of the fusion model were compared with Medical Subject Headings, a traditional and commonly used disease taxonomy. Then, experimental results of model performance comparison show that our approach performs better than other integration methods. As it was observed, the obtained clusters provided more interesting and novel disease-disease associations. This multi-view human disease association network describes relationships between diseases based on multiple molecular levels, thus breaking through the limitation of the disease classification system based on tissues and organs. This approach which motivates clinicians and researchers to reposition the understanding of diseases and explore diagnosis and therapy strategies, extends the existing disease taxonomy.

Availability of data and materials

The preprocessed dataset and source code supporting the conclusions of this article are available at GitHub repository https://github.com/yangxiaoxi89/mvHDN.

MSCNE:Predict miRNA-Disease Associations Using Neural Network Based on Multi-Source Biological Information

The important role of microRNA (miRNA) in human diseases has been confirmed by some studies. However, only using biological experiments has greater blindness, leading to higher experimental costs. In this paper a high-efficiency algorithm based on a variety of biological source information and applying a combination of a convolutional neural network (CNN) feature extractor and an extreme learning machine (ELM) classifier is proposed. Specifically, the semantic similarity of diseases, the gaussian interaction profile kernel similarity of the four biological information of miRNA, disease, long non-coding RNA (lncRNA) and environmental factors (EFs), and the similarities of miRNAs are fused together. Among them, miRNAs similarity is composed of miRNA target information, sequence information, family information, and function information. Then, the dimensionality of the data set is reduced by the autoencoder (AE). Finally, deep features are extracted through CNN, and then the association between miRNA and disease is predicted by ELM. The experimental results show that the average AUC value based on the multi-biological source information (MSCNE) model is 0.9630, which can reach higher performance than the other classic classifier, feature extractor mentioned and the other existing algorithms. The results show the MSCNE algorithm is effective to predict the correlation of miRNA-disease.

Biomedical data, computational methods and tools for evaluating disease-disease associations

In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.

Hypergraph-based logistic matrix factorization for metabolite-disease interaction prediction

MOTIVATION

Function-related metabolites, the terminal products of the cell regulation, show a close association with complex diseases. The identification of disease-related metabolites is critical to the diagnosis, prevention and treatment of diseases. However, most existing computational approaches build networks by calculating pairwise relationships, which is inappropriate for mining higher-order relationships.

RESULTS

In this study, we presented a novel approach with hypergraph-based logistic matrix factorization, HGLMF, to predict the potential interactions between metabolites and disease. First, the molecular structures and gene associations of metabolites and the hierarchical structures and GO functional annotations of diseases were extracted to build various similarity measures of metabolites and diseases. Next, the kernel neighborhood similarity of metabolites (or diseases) was calculated according to the completed interactive network. Second, multiple networks of metabolites and diseases were fused, respectively, and the hypergraph structures of metabolites and diseases were built. Finally, a logistic matrix factorization based on hypergraph was proposed to predict potential metabolite-disease interactions. In computational experiments, HGLMF accurately predicted the metabolite-disease interaction, and performed better than other state-of-the-art methods. Moreover, HGLMF could be used to predict new metabolites (or diseases). As suggested from the case studies, the proposed method could discover novel disease-related metabolites, which has been confirmed in existing studies.

AVAILABILITY AND IMPLEMENTATION

The codes and dataset are available at: https://github.com/Mayingjun20179/HGLMF.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Multiple Laplacian Regularized RBF Neural Network for Assessing Dry Weight of Patients With End-Stage Renal Disease

Dry weight (DW) is an important dialysis index for patients with end-stage renal disease. It can guide clinical hemodialysis. Brain natriuretic peptide, chest computed tomography image, ultrasound, and bioelectrical impedance analysis are key indicators (multisource information) for assessing DW. By these approaches, a trial-and-error method (traditional measurement method) is employed to assess DW. The assessment of clinician is time-consuming. In this study, we developed a method based on artificial intelligence technology to estimate patient DW. Based on the conventional radial basis function neural (RBFN) network, we propose a multiple Laplacian-regularized RBFN (MLapRBFN) model to predict DW of patient. Compared with other model and body composition monitor, our method achieves the lowest value (1.3226) of root mean square error. In Bland-Altman analysis of MLapRBFN, the number of out agreement interval is least (17 samples). MLapRBFN integrates multiple Laplace regularization terms, and employs an efficient iterative algorithm to solve the model. The ratio of out agreement interval is 3.57%, which is lower than 5%. Therefore, our method can be tentatively applied for clinical evaluation of DW in hemodialysis patients.