Novel human microbe-disease associations inference based on network consistency projection

Zou, Shuai; Zhang, Jingpu; Zhang, Zuping

doi:10.1038/s41598-018-26448-8

Cited by 13 publications

(9 citation statements)

References 54 publications

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“…NCP was first proposed by Gu et al (2016) to predict miRNA-disease association. Zou et al (2018) used this method to predict the association between microorganisms and diseases and achieved good results. NCP is a method based on a general nonparametric network, which belongs to the category of unsupervised learning.…”

Section: Resultsmentioning

confidence: 99%

Repositioning Drugs on Human Influenza A Viruses Based on a Novel Nuclear Norm Minimization Method

et al. 2021

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Influenza A viruses, especially H3N2 and H1N1 subtypes, are viruses that often spread among humans and cause influenza pandemic. There have been several big influenza pandemics that have caused millions of human deaths in history, and the threat of influenza viruses to public health is still serious nowadays due to the frequent antigenic drift and antigenic shift events. However, only few effective anti-flu drugs have been developed to date. The high development cost, long research and development time, and drug side effects are the major bottlenecks, which could be relieved by drug repositioning. In this study, we proposed a novel antiviral Drug Repositioning method based on minimizing Matrix Nuclear Norm (DRMNN). Specifically, a virus-drug correlation database consisting of 34 viruses and 205 antiviral drugs was first curated from public databases and published literature. Together with drug similarity on chemical structure and virus sequence similarity, we formulated the drug repositioning problem as a low-rank matrix completion problem, which was solved by minimizing the nuclear norm of a matrix with a few regularization terms. DRMNN was compared with three recent association prediction algorithms. The AUC of DRMNN in the global fivefold cross-validation (fivefold CV) is 0.8661, and the AUC in the local leave-one-out cross-validation (LOOCV) is 0.6929. Experiments have shown that DRMNN is better than other algorithms in predicting which drugs are effective against influenza A virus. With H3N2 as an example, 10 drugs most likely to be effective against H3N2 viruses were listed, among which six drugs were reported, in other literature, to have some effect on the viruses. The protein docking experiments between the chemical structure of the prioritized drugs and viral hemagglutinin protein also provided evidence for the potential of the predicted drugs for the treatment of influenza.

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Section: Resultsmentioning

confidence: 99%

Repositioning Drugs on Human Influenza A Viruses Based on a Novel Nuclear Norm Minimization Method

et al. 2021

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“…The Human Microbe Disease Association Database (HMDAD) was the first resource developed using literature mining to systematically gather experimental data to study microbe–disease associations (Ma et al, 2017b). Several tools have been developed thereafter to utilize the curated data from HMDAD and score human microbe associations using advanced mathematical approaches (Chen et al, 2017; Huang et al, 2017a; Huang et al, 2017b; Wang et al, 2017b; Peng et al, 2018; Zou et al, 2018; Qu et al, 2019). The above set of tools focuses on identifying associated genera across a set of selected diseases and is eventually used to find diseases having similar pattern of associated microbes.…”

Section: Introductionmentioning

confidence: 99%

“EviMass”: A Literature Evidence-Based Miner for Human Microbial Associations

et al. 2019

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The importance of understanding microbe–microbe as well as microbe–disease associations is one of the key thrust areas in human microbiome research. High-throughput metagenomic and transcriptomic projects have fueled discovery of a number of new microbial associations. Consequently, a plethora of information is being added routinely to biomedical literature, thereby contributing toward enhancing our knowledge on microbial associations. In this communication, we present a tool called “EviMass” (Evidence based mining of human Microbial Associations), which can assist biologists to validate their predicted hypotheses from new microbiome studies. Users can interactively query the processed back-end database for microbe–microbe and disease–microbe associations. The EviMass tool can also be used to upload microbial association networks generated from a human “disease–control” microbiome study and validate the associations from biomedical literature. Additionally, a list of differentially abundant microbes for the corresponding disease can be queried in the tool for reported evidences. The results are presented as graphical plots, tabulated summary, and other evidence statistics. EviMass is a comprehensive platform and is expected to enable microbiome researchers not only in mining microbial associations, but also enriching a new research hypothesis. The tool is available free for academic use at .

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“…Some similarity calculation methods of diseases have been proposed using different kinds of disease information. Symptom-based disease similarity has been increasingly demonstrated that it can provide effective information for MDA prediction ( Peng et al, 2018b ; Zou et al, 2018 ). In this work, we also introduced symptom-based disease similarity and utilized

to represent the similarity matrix.…”

Section: Methodsmentioning

confidence: 99%

Novel Collaborative Weighted Non-negative Matrix Factorization Improves Prediction of Disease-Associated Human Microbes

Zhang

et al. 2022

Front. Microbiol.

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Extensive clinical and biomedical studies have shown that microbiome plays a prominent role in human health. Identifying potential microbe–disease associations (MDAs) can help reveal the pathological mechanism of human diseases and be useful for the prevention, diagnosis, and treatment of human diseases. Therefore, it is necessary to develop effective computational models and reduce the cost and time of biological experiments. Here, we developed a novel machine learning-based joint framework called CWNMF-GLapRLS for human MDA prediction using the proposed collaborative weighted non-negative matrix factorization (CWNMF) technique and graph Laplacian regularized least squares. Especially, to fuse more similarity information, we calculated the functional similarity of microbes. To deal with missing values and effectively overcome the data sparsity problem, we proposed a collaborative weighted NMF technique to reconstruct the original association matrix. In addition, we developed a graph Laplacian regularized least-squares method for prediction. The experimental results of fivefold and leave-one-out cross-validation demonstrated that our method achieved the best performance by comparing it with 5 state-of-the-art methods on the benchmark dataset. Case studies further showed that the proposed method is an effective tool to predict potential MDAs and can provide more help for biomedical researchers.

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Novel human microbe-disease associations inference based on network consistency projection

Cited by 13 publications

References 54 publications

Repositioning Drugs on Human Influenza A Viruses Based on a Novel Nuclear Norm Minimization Method

Repositioning Drugs on Human Influenza A Viruses Based on a Novel Nuclear Norm Minimization Method

“EviMass”: A Literature Evidence-Based Miner for Human Microbial Associations

Novel Collaborative Weighted Non-negative Matrix Factorization Improves Prediction of Disease-Associated Human Microbes

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