HMDD v3.0: a database for experimentally supported human microRNA–disease associations

Huang, Zhou; Shi, Jiangcheng; Gao, Yuanxu; Cui, Chunmei; Zhang, Shan; Li, Jianwei; Zhou, Yuan; Cui, Qinghua

doi:10.1093/nar/gky1010

Cited by 609 publications

(375 citation statements)

References 30 publications

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“…The results showed that the majority of these genes appeared in cancer‐related pathways (, A–H). Moreover, the pathway‐related miRNAs were annotated to the corresponding cancers in the HMDD (Human microRNA Disease Database v3.0; Huang et al ., ; , ). As shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

Topologically inferring active miRNA‐mediated subpathways toward precise cancer classification by directed random walk

et al. 2019

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Accurate predictions of classification biomarkers and disease status are indispensable for clinical cancer diagnosis and research. However, the robustness of conventional gene biomarkers is limited by issues with reproducibility across different measurement platforms and cohorts of patients. In this study, we collected 4775 samples from 12 different cancer datasets, which contained 4636 TCGA samples and 139 GEO samples. A new method was developed to detect miRNA‐mediated subpathway activities by using directed random walk (miDRW). To calculate the activity of each miRNA‐mediated subpathway, we constructed a global directed pathway network (GDPN) with genes as nodes. We then identified miRNAs with expression levels which were strongly inversely correlated with differentially expressed target genes in the GDPN. Finally, each miRNA‐mediated subpathway activity was integrated with the topological information, differential levels of miRNAs and genes, expression levels of genes, and target relationships between miRNAs and genes. The results showed that the proposed method yielded a more robust and accurate overall performance compared with other existing pathway‐based, miRNA‐based, and gene‐based classification methods. The high‐frequency miRNA‐mediated subpathways are more reliable in classifying samples and for selecting therapeutic strategies.

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Section: Resultsmentioning

confidence: 99%

Topologically inferring active miRNA‐mediated subpathways toward precise cancer classification by directed random walk

et al. 2019

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“…Novel datasets were obtained to build our enrichment categories, consisting of Gene Ontology [25], miRTarBase 8.0 [15], KEGG [26], miRandola 2017 [22], miRPathDB 2.0 [16], TissueAtlas [17], MNDR v2.0 [19], NPInter 4.0 [27], RNALocate v2.0 [21], TAM 2.0 [13], and TransmiR v2.0 [20]. Other pre-existing datasets have been updated, including HMDD v3.0 [18] and miRBase v22.1 [28]. We retained the rest of our pre-existing datasets, namely miRWalk2.0 [29], our published age and gender dependent miRNAs and our distribution of miRNAs in immune cells [11].…”

Section: Data Collectionmentioning

confidence: 99%

“…All kinds of enrichment tools rely on high quality sets of miRNA categories that were either obtained by curation of scientific literature or collected from specific databases. For instance, curated miRNA annotations can be obtained from miRBase or miRCarta [14], miRNA-target interactions from miRTarBase [15], miRNA-pathway associations from miRPathDB [16], tissuespecific miRNAs from the human TissueAtlas [17], or miRNA-disease associations from HMDD [18] or MNDR [19], many of which were updated in the last two years. Further specialised annotations like miRNA and transcription factor interactions provided by TransmiR [20], miRNA sub-cellular localisations collected in RNALocate [21], or extra-cellular circulating miRNAs contained in miRandola [22] provide target categories for integrated enrichment analysis.…”

Section: Introductionmentioning

confidence: 99%

miEAA 2.0: Integrating multi-species microRNA enrichment analysis and workflow management systems

Kern

Fehlmann

Solomon

et al. 2020

Preprint

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AbstractGene set enrichment analysis has become one of the most frequently used applications in molecular biology research. Originally developed for gene sets, the same statistical principles are now available for all omics types. In 2016, we published the miRNA enrichment analysis and annotation tool (miEAA) for human precursor and mature miRNAs.Here, we present miEAA 2.0, supporting miRNA input from Homo sapiens, Mus musculus, and Rattus norvegicus. To facilitate inclusion of miEAA in workflow systems, we implemented an Application Programming Interface (API). Users can perform miRNA set enrichment analysis using either the web-interface, a dedicated Python package, or custom remote clients. Moreover, the number of category sets was raised by an order of magnitude. We implemented novel categories like annotation confidence level or localisation in biological compartments. In combination with the miR-Base miRNA-version and miRNA-to-precursor converters, miEAA supports research settings where older releases of miRBase are in use. The web server also offers novel comprehensive visualisations such as heatmaps and running sum curves with background distributions. Lastly, additional methods to correct for multiple hypothesis testing were implemented. We demonstrate the new features using case studies for human kidney cancer and mouse samples. The tool is freely accessible at: https://www.ccb.uni-saarland.de/mieaa2.

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“…35,547 known human miRNA-disease associations which consist of 1206 different miRNAs and 894 different diseases were downloaded from HMDD in May 8, 2019 (Huang, et al, 2018). Considering that the name of disease and miRNA in the original database are nonstandard such as "breast neoplasms" and "carcinoma, breast" are the same type of disease.…”

Section: Known Mirna-disease Associationsmentioning

confidence: 99%

MeSHHeading2vec: A new method for representing MeSH headings as feature vectors based on graph embedding algorithm

Guo

You

et al. 2019

Preprint

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Motivation: Effectively representing the MeSH headings (terms) such as disease and drug as discriminative vectors could greatly improve the performance of downstream computational prediction models. However, these terms are often abstract and difficult to quantify. Results: In this paper, we converted the MeSH tree structure into a relationship network and applied several graph embedding algorithms on it to represent these terms. Specifically, the relationship network consisting of nodes (MeSH headings) and edges (relationships) which can be constructed by the rule of tree num. Then, five graph embedding algorithms including DeepWalk (DW), LINE, SDNE, LAP and HOPE were implemented on the relationship network to represent MeSH headings as vectors. In order to evaluate the performance of the proposed method, we carried out the node classification and relationship prediction tasks. The experimental results show that the MeSH headings characterized by graph embedding algorithms can not only be treated as an independent carrier for representation, but also can be utilized as additional information to enhance the distinguishable ability of vectors. Thus, it can act as input and continue to play a significant role in any disease-, drug-, microbe-and etc.-related computational models. Besides, our method holds great hope to inspire relevant researchers to study the representation of terms in this network perspective. Contact: zhuhongyou@ms.xjb.ac.cn

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HMDD v3.0: a database for experimentally supported human microRNA–disease associations

Cited by 609 publications

References 30 publications

Topologically inferring active miRNA‐mediated subpathways toward precise cancer classification by directed random walk

Topologically inferring active miRNA‐mediated subpathways toward precise cancer classification by directed random walk

miEAA 2.0: Integrating multi-species microRNA enrichment analysis and workflow management systems

MeSHHeading2vec: A new method for representing MeSH headings as feature vectors based on graph embedding algorithm

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