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Mendeley readers
Attention Score in Context
| Title |
A novel Markov Blanket-based repeated-fishing strategy for capturing phenotype-related biomarkers in big omics data
|
|---|---|
| Published in |
BMC Genomic Data, March 2016
|
| DOI | 10.1186/s12863-016-0358-5 |
| Pubmed ID | |
| Authors |
Hongkai Li, Zhongshang Yuan, Jiadong Ji, Jing Xu, Tao Zhang, Xiaoshuai Zhang, Fuzhong Xue |
| Abstract |
We propose a novel Markov Blanket-based repeated-fishing strategy (MBRFS) in attempt to increase the power of existing Markov Blanket method (DASSO-MB) and maintain its advantages in omic data analysis. Both simulation and real data analysis were conducted to assess its performances by comparing with other methods including χ (2) test with Bonferroni and B-H adjustment, least absolute shrinkage and selection operator (LASSO) and DASSO-MB. A serious of simulation studies showed that the true discovery rate (TDR) of proposed MBRFS was always close to zero under null hypothesis (odds ratio = 1 for each SNPs) with excellent stability in all three scenarios of independent phenotype-related SNPs without linkage disequilibrium (LD) around them, correlated phenotype-related SNPs without LD around them, and phenotype-related SNPs with strong LD around them. As expected, under different odds ratio and minor allel frequency (MAFs), MBRFS always had the best performances in capturing the true phenotype-related biomarkers with higher matthews correlation coefficience (MCC) for all three scenarios above. More importantly, since proposed MBRFS using the repeated fishing strategy, it still captures more phenotype-related SNPs with minor effects when non-significant phenotype-related SNPs emerged under χ (2) test after Bonferroni multiple correction. The various real omics data analysis, including GWAS data, DNA methylation data, gene expression data and metabolites data, indicated that the proposed MBRFS always detected relatively reasonable biomarkers. Our proposed MBRFS can exactly capture the true phenotype-related biomarkers with the reduction of false negative rate when the phenotype-related biomarkers are independent or correlated, as well as the circumstance that phenotype-related biomarkers are associated with non-phenotype-related ones. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 28 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 21% |
| Researcher | 6 | 21% |
| Student > Master | 3 | 11% |
| Student > Postgraduate | 2 | 7% |
| Professor > Associate Professor | 2 | 7% |
| Other | 2 | 7% |
| Unknown | 7 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 6 | 21% |
| Biochemistry, Genetics and Molecular Biology | 5 | 18% |
| Nursing and Health Professions | 3 | 11% |
| Neuroscience | 2 | 7% |
| Engineering | 2 | 7% |
| Other | 4 | 14% |
| Unknown | 6 | 21% |
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 16 March 2016.
All research outputs
#19,944,091
of 25,373,627 outputs
Outputs from BMC Genomic Data
#786
of 1,204 outputs
Outputs of similar age
#218,219
of 314,759 outputs
Outputs of similar age from BMC Genomic Data
#23
of 40 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,204 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 28th percentile – i.e., 28% of its peers scored the same or lower than it.
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We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.