miR2Disease: a manually curated database for microRNA deregulation in human disease

By Qinghua Jiang, Yadong Wang, Y. Hao, Liran Juan, Mingxiang Teng, Xinjun Zhang, Meimei Li, Guohua Wang, Yunlong Liu. Nucleic Acids Res., 2008

1,388 citations recorded by the source corpus.

‘miR2Disease’, a manually curated database, aims at providing a comprehensive resource of microRNA deregulation in various human diseases. The current version of miR2Disease documents 1939 curated relationships between 299 human microRNAs and 94 human diseases by reviewing more than 600 published papers. Around one-seventh of the microRNA–disease relationships represent the pathogenic roles of deregulated microRNA in human disease. Each entry in the miR2Disease contains detailed information on a microRNA–disease relationship, including a microRNA ID, the disease name, a brief description of the microRNA–disease relationship, an expression pattern of the microRNA, the detection method for microRNA expression, experimentally verified target gene(s) of the microRNA and a literature reference. miR2Disease provides a user-friendly interface for a convenient retrieval of each entry by microRNA ID, disease name, or target gene. In addition, miR2Disease offers a submission page that allows researchers to submit established microRNA–disease relationships that are not documented. Once approved by the submission review committee, the submitted records will be included in the database. miR2Disease is freely available at http://www.miR2Disease.org.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e03a1c0f0a8d95aa84bb21f2aa95e5053e4cb655

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Xing Chen
2. Zhuhong You
3. Maria D. Paraskevopoulou
4. Chenggang Clarence Yan
5. Artemis G. Hatzigeorgiou
6. Xu Zhang
7. Qinghua Cui
8. Martin Reczko

In Molecular Biology

1. Xing Chen
2. Zhuhong You
3. Chenggang Clarence Yan
4. Maria D. Paraskevopoulou
5. Xu Zhang
6. Artemis G. Hatzigeorgiou
7. Qinghua Cui
8. Martin Reczko

Citations in the corpus

• Mechanisms of diabetic complications. (2013, background): “miRNAs are involved in the normal functioning of all eukaryotic cells and so similarly their dysregulation is known to contribute to a number of disease processes (274) including diabetes complications (289).”
• TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support (2011, background): “miR2Disease (10) was first released in 2008.”
• Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors (2013, background): “Second, miR2Disease is a manually curated database which provides a comprehensive resource of miRNA deregulation in various human diseases [37].”
• Identifying miRNAs, targets and functions (2012, methodology): “Few other tools, such as miR2Disease [69] and miReg [70], manually curate annotation of miRNA functions and focus on the association”
• Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression. (2010, background): “miRNAs are reported to be involved in 94 human diseases (Jiang et al. 2009), ranging from psychiatric disorders (Barbato et al.”
• WBSMDA: Within and Between Score for MiRNA-Disease Association prediction (2016, methodology): “The prediction results were validated based on another two important miRNA-disease association databases, miR2Disease 52 and dbDEMC database 53 .”
• The multiMiR R package and database: integration of microRNA–target interactions along with their disease and drug associations (2014, background): “To facilitate biomedical research, a few online resources, such as miR2Disease (22), Pharmaco-miR (23) and PhenomiR (24), are dedicated to disease-or drug-related miRNAs from the literature.”
• Semi-supervised learning for potential human microRNA-disease associations inference (2014, neutral): “To provide a comprehensive resource of experimentally verified miRNA-disease associations, Lu, et al. 30 and Jiang, et al. 48 successively constructed two publicly available and manually curated databases, i.e. Human MicroRNA Disease Database (HMDD) and miR2Disease.”
• miRWalk - Database: Prediction of possible miRNA binding sites by "walking" the genes of three genomes (2011, methodology): “We compared Validated Target module of miRWalk with TarBase, miR2Disease [44], miRecords, PhenomiR [45] and miRTarBase databases in terms of relationships documented on miRNAs,”
• HMDD v3.0: a database for experimentally supported human microRNA–disease associations (2018, background): “Furthermore, besides HMDD, there are also some other miRNA–disease databases, such as miR2Disease (18), PhenomiR (28), dbDEMC (29), the data in HMDD could be used to rapidly update these databases by their authors.”
• miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions (2017, neutral): “HMDD (7) and miR2Disease (8) contain experimentally validated human miRNA and disease associated MTIs.”
• miRTarBase 2020: updates to the experimentally validated microRNA–target interaction database (2019, background): “miR2Disease can provide users disease-associated MTIs based on the information on miRNA–disease relationships obtained from research articles (11).”