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Attention Score in Context
| Title |
Study design and data analysis considerations for the discovery of prognostic molecular biomarkers: a case study of progression free survival in advanced serous ovarian cancer
|
|---|---|
| Published in |
BMC Medical Genomics, June 2016
|
| DOI | 10.1186/s12920-016-0187-4 |
| Pubmed ID | |
| Authors |
Li-Xuan Qin, Douglas A. Levine |
| Abstract |
Accurate discovery of molecular biomarkers that are prognostic of a clinical outcome is an important yet challenging task, partly due to the combination of the typically weak genomic signal for a clinical outcome and the frequently strong noise due to microarray handling effects. Effective strategies to resolve this challenge are in dire need. We set out to assess the use of careful study design and data normalization for the discovery of prognostic molecular biomarkers. Taking progression free survival in advanced serous ovarian cancer as an example, we conducted empirical analysis on two sets of microRNA arrays for the same set of tumor samples: arrays in one set were collected using careful study design (that is, uniform handling and randomized array-to-sample assignment) and arrays in the other set were not. We found that (1) handling effects can confound the clinical outcome under study as a result of chance even with randomization, (2) the level of confounding handling effects can be reduced by data normalization, and (3) good study design cannot be replaced by post-hoc normalization. In addition, we provided a practical approach to define positive and negative control markers for detecting handling effects and assessing the performance of a normalization method. Our work showcased the difficulty of finding prognostic biomarkers for a clinical outcome of weak genomic signals, illustrated the benefits of careful study design and data normalization, and provided a practical approach to identify handling effects and select a beneficial normalization method. Our work calls for careful study design and data analysis for the discovery of robust and translatable molecular biomarkers. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Greece | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 12 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 3 | 25% |
| Researcher | 3 | 25% |
| Student > Ph. D. Student | 2 | 17% |
| Professor | 1 | 8% |
| Unknown | 3 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 3 | 25% |
| Biochemistry, Genetics and Molecular Biology | 1 | 8% |
| Psychology | 1 | 8% |
| Sports and Recreations | 1 | 8% |
| Decision Sciences | 1 | 8% |
| Other | 2 | 17% |
| Unknown | 3 | 25% |
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 22 July 2016.
All research outputs
#18,466,238
of 22,881,154 outputs
Outputs from BMC Medical Genomics
#863
of 1,224 outputs
Outputs of similar age
#260,399
of 345,191 outputs
Outputs of similar age from BMC Medical Genomics
#8
of 10 outputs
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So far Altmetric has tracked 1,224 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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