In silico prediction of potential miRNA‐disease association using an integrative bioinformatics approach based on kernel fusion

Guan, Na-Na; Wang, Chun-Chun; Zhang, Li; Huang, Li; Li, Jianqiang; Piao, Xue

doi:10.1111/jcmm.14765

Cited by 8 publications

(4 citation statements)

References 76 publications

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“…These diseases were selected in our case study because they all have high incidence and insignificant early symptoms. In addition, they have been considered as case studies in many previous publications (Guan et al, 2020). Our case study used HMDD v2.0 as the training database for QIMCMDA.…”

Section: Case Studymentioning

confidence: 99%

“…Therefore, the main problem of the model is the lack of negative samples, which will make the supervised learning model unsuitable for the prediction of large-scale disease-miRNA interactions. Obtaining large numbers of negatively associated samples is still difficult (Guan et al, 2020). Chen et al (2012) adopted restart random walk (RWRMDA) to predict the potential miRNA-disease interaction, which restarted the known miRNAdisease interaction network, using random walks on miRNA functional similarity network to predict potential miRNA-disease interaction.…”

Section: Introductionmentioning

confidence: 99%

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QIMCMDA: MiRNA-Disease Association Prediction by q-Kernel Information and Matrix Completion

et al. 2020

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Studies have shown that microRNAs (miRNAs) are closely associated with many human diseases, but we have not yet fully understand the role and potential molecular mechanisms of miRNAs in the process of disease development. However, ordinary biological experiments often require higher costs, and computational methods can be used to quickly and effectively predict the potential miRNA-disease association effect at a lower cost, and can be used as a useful reference for experimental methods. For miRNA-disease association prediction, we have proposed a new method called Matrix completion algorithm based on q-kernel information (QIMCMDA). We use fivefold crossvalidation and leave-one-out cross-validation to prove the effectiveness of QIMCMDA. LOOCV shows that AUC can reach 0.9235, and its performance is significantly better than other commonly used technologies. In addition, we applied QIMCMDA to case studies of three human diseases, and the results show that our method performs well in inferring potential interaction between miRNAs and diseases. It is expected that QIMCMDA will become an excellent supplement in the field of biomedical research in the future.

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Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

QIMCMDA: MiRNA-Disease Association Prediction by q-Kernel Information and Matrix Completion

et al. 2020

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“…Network pharmacology (NP) in traditional Chinese medicine (TCM) is a technology that combines various fields, including computer science, systems biology, and pharmacology, which offers a unique network mode comprising "multiple targets, multiple effects, and complicated diseases" [10]. It associates drugs and diseases in a broader sense, and it provides different approaches for investigating the mechanisms of traditional Chinese medicine by introducing and developing new drugs [11]. Bioinformatics is an innovative field that integrates molecular biology with mathematics, statistics, computer science, and other disciplines.…”

Section: Introductionmentioning

confidence: 99%

Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation Analysis Reveal Insights into the Molecular Mechanism of Cordia myxa in the Treatment of Liver Cancer

Li,

Mohammed,

Auda

et al. 2024

Biology

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Traditional treatments of cancer have faced various challenges, including toxicity, medication resistance, and financial burdens. On the other hand, bioactive phytochemicals employed in complementary alternative medicine have recently gained interest due to their ability to control a wide range of molecular pathways while being less harmful. As a result, we used a network pharmacology approach to study the possible regulatory mechanisms of active constituents of Cordia myxa for the treatment of liver cancer (LC). Active constituents were retrieved from the IMPPAT database and the literature review, and their targets were retrieved from the STITCH and Swiss Target Prediction databases. LC-related targets were retrieved from expression datasets (GSE39791, GSE76427, GSE22058, GSE87630, and GSE112790) through gene expression omnibus (GEO). The DAVID Gene Ontology (GO) database was used to annotate target proteins, while the Kyoto Encyclopedia and Genome Database (KEGG) was used to analyze signaling pathway enrichment. STRING and Cytoscape were used to create protein–protein interaction networks (PPI), while the degree scoring algorithm of CytoHubba was used to identify hub genes. The GEPIA2 server was used for survival analysis, and PyRx was used for molecular docking analysis. Survival and network analysis revealed that five genes named heat shot protein 90 AA1 (HSP90AA1), estrogen receptor 1 (ESR1), cytochrome P450 3A4 (CYP3A4), cyclin-dependent kinase 1 (CDK1), and matrix metalloproteinase-9 (MMP9) are linked with the survival of LC patients. Finally, we conclude that four extremely active ingredients, namely cosmosiin, rosmarinic acid, quercetin, and rubinin influence the expression of HSP90AA1, which may serve as a potential therapeutic target for LC. These results were further validated by molecular dynamics simulation analysis, which predicted the complexes with highly stable dynamics. The residues of the targeted protein showed a highly stable nature except for the N-terminal domain without affecting the drug binding. An integrated network pharmacology and docking study demonstrated that C. myxa had a promising preventative effect on LC by working on cancer-related signaling pathways.

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“…It can be used to study the relationship and laws of biological genes and diseases. In addition, it has rapidly developed into the most attractive frontier of life sciences today ( 12 , 13 ). Molecular docking plays an important guiding role in the development and design of drugs and may provide keen insights in protein function prediction and other important issues.…”

Section: Introductionmentioning

confidence: 99%

Mechanism of Phellodendron and Anemarrhena Drug Pair on the Treatment of Liver Cancer Based on Network Pharmacology and Bioinformatics

Ruan

et al. 2022

Front. Oncol.

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BackgroundThis study aims to explore the key targets and signaling pathways of the traditional Chinese medicine Phellodendron and Anemarrhena drug pair (PADP) for the treatment of liver cancer.MethodsFirstly, bioinformatics technology was used to analyze GSE62232 gene chip to obtain the differential genes of liver cancer. A network pharmacology technology was used to find the active components of PADP and their targets. Secondly, the differential genes were imported into STRING database to draw a PPI network, and network topology structure map combined with Cytoscape software. And the R language was used to identify differential gene targets and pathways through GO and KEGG pathway enrichment analysis. In addition, AutoDock Vina was used for molecular docking of core targets and core compounds. Moreover, GEPIA online analysis tool was used to perform survival analysis of the core target genes. Finally, RT-PCR was used to verify the changes of key target genes. CCK−8 assay was performed to detect cell proliferation. Flow cytometry was performed to detect the cell cycle and apoptotic. Transwell invasion assay was performed to detect cell invasion.ResultsFirstly, a total of 21,654 genes were obtained. After screening, 1019 differential genes were obtained, including 614 down-regulated genes and 405 up-regulated genes. Furthermore, after screening by ADME standards, 52 active ingredients were obtained, of which 37 were Phellodendron and 15 were Anemarrhena. And a total of 36 differential genes have been identified, including 13 up-regulated genes and 23 down-regulated genes. Moreover, through enrichment analysis, we found that PADP may treat liver cancer through multiple channels and multiple pathways including the p53 signaling pathway, IL-17 signaling pathway, TNF signaling pathway, Toll-like receptor signaling pathway and so on. Secondly, the molecular docking results showed that there was certain affinity between the core compounds and core target genes. In addition, GEPIA online analysis showed that ESR1, AR, CCNB1, CDK1, AKR1C3 and CCNA2 might become potential target genes for the survival and prognosis of PADP for the treatment of liver cancer. Finally, it was found that PADP could up regulate genes ESR1 and AR, down regulate genes CCNB1, CDK1, AKR1C3, and CCNA2. PADP could promote the apoptosis of liver cancer cells, shorten the cell cycle, and inhibit the proliferation and invasion of liver cancer cells.ConclusionPADP may treat liver cancer through multiple targets, multiple channels, and multiple pathways, thereby suppressing cancer cells and improving the living quality of patients.

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In silico prediction of potential miRNA‐disease association using an integrative bioinformatics approach based on kernel fusion

Cited by 8 publications

References 76 publications

QIMCMDA: MiRNA-Disease Association Prediction by q-Kernel Information and Matrix Completion

QIMCMDA: MiRNA-Disease Association Prediction by q-Kernel Information and Matrix Completion

Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation Analysis Reveal Insights into the Molecular Mechanism of Cordia myxa in the Treatment of Liver Cancer

Mechanism of Phellodendron and Anemarrhena Drug Pair on the Treatment of Liver Cancer Based on Network Pharmacology and Bioinformatics

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