| Title |
Mapping circulating serum miRNAs to their immune-related target mRNAs
|
|---|---|
| Published in |
Advances and Applications in Bioinformatics and Chemistry : AABC, February 2017
|
| DOI | 10.2147/aabc.s121598 |
| Pubmed ID | |
| Authors |
Bakhtiyor Nosirov, Joël Billaud, Alexis Vandenbon, Diego Diez, Edward Wijaya, Ken J Ishii, Shunsuke Teraguchi, Daron M Standley |
| Abstract |
Evidence suggests that circulating serum microRNAs (miRNAs) might preferentially target immune-related mRNAs. If this were the case, we hypothesized that immune-related mRNAs would have more predicted serum miRNA binding sites than other mRNAs and, reciprocally, that serum miRNAs would have more immune-related mRNA targets than non-serum miRNAs. We developed a consensus target predictor using the random forest framework and calculated the number of predicted miRNA-mRNA interactions in various subsets of miRNAs (serum, non-serum) and mRNAs (immune related, nonimmune related). Immune-related mRNAs were predicted to be targeted by serum miRNA more than other mRNAs. Moreover, serum miRNAs were predicted to target many more immune-related mRNA targets than non-serum miRNAs; however, these two biases in immune-related mRNAs and serum miRNAs appear to be completely independent. Immune-related mRNAs have more miRNA binding sites in general, not just for serum miRNAs; likewise, serum miRNAs target many more mRNAs than non-serum miRNAs overall, regardless of whether they are immune related or not. Nevertheless, these two independent phenomena result in a significantly larger number of predicted serum miRNA-immune mRNA interactions than would be expected by chance. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 13 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 4 | 31% |
| Student > Bachelor | 2 | 15% |
| Student > Master | 1 | 8% |
| Professor | 1 | 8% |
| Professor > Associate Professor | 1 | 8% |
| Other | 1 | 8% |
| Unknown | 3 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 3 | 23% |
| Agricultural and Biological Sciences | 3 | 23% |
| Medicine and Dentistry | 2 | 15% |
| Nursing and Health Professions | 1 | 8% |
| Veterinary Science and Veterinary Medicine | 1 | 8% |
| Other | 2 | 15% |
| Unknown | 1 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 February 2017.
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#22,764,772
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Outputs from Advances and Applications in Bioinformatics and Chemistry : AABC
#41
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Outputs of similar age from Advances and Applications in Bioinformatics and Chemistry : AABC
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