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Attention Score in Context
| Title |
An adaptive genetic algorithm for selection of blood-based biomarkers for prediction of Alzheimer's disease progression
|
|---|---|
| Published in |
BMC Bioinformatics, December 2015
|
| DOI | 10.1186/1471-2105-16-s18-s1 |
| Pubmed ID | |
| Authors |
Luke Vandewater, Vladimir Brusic, William Wilson, Lance Macaulay, Ping Zhang |
| Abstract |
Alzheimer's disease is a multifactorial disorder that may be diagnosed earlier using a combination of tests rather than any single test. Search algorithms and optimization techniques in combination with model evaluation techniques have been used previously to perform the selection of suitable feature sets. Previously we successfully applied GA with LR to neuropsychological data contained within the The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging, to select cognitive tests for prediction of progression of AD. This research addresses an Adaptive Genetic Algorithm (AGA) in combination with LR for identifying the best biomarker combination for prediction of the progression to AD. The model has been explored in terms of parameter optimization to predict conversion from healthy stage to AD with high accuracy. Several feature sets were selected - the resulting prediction moddels showed higher area under the ROC values (0.83-0.89). The results has shown consistency with some of the medical research reported in literature. The AGA has proven useful in selecting the best combination of biomarkers for prediction of AD progression. The algorithm presented here is generic and can be extended to other data sets generated in projects that seek to identify combination of biomarkers or other features that are predictive of disease onset or progression. |
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 % |
|---|---|---|
| United States | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 3% |
| Unknown | 39 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 9 | 23% |
| Researcher | 7 | 18% |
| Student > Ph. D. Student | 6 | 15% |
| Student > Bachelor | 2 | 5% |
| Lecturer | 2 | 5% |
| Other | 4 | 10% |
| Unknown | 10 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 6 | 15% |
| Neuroscience | 4 | 10% |
| Computer Science | 4 | 10% |
| Engineering | 3 | 8% |
| Medicine and Dentistry | 3 | 8% |
| Other | 8 | 20% |
| Unknown | 12 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 30 July 2016.
All research outputs
#2,822,997
of 22,836,570 outputs
Outputs from BMC Bioinformatics
#962
of 7,288 outputs
Outputs of similar age
#49,461
of 389,033 outputs
Outputs of similar age from BMC Bioinformatics
#19
of 153 outputs
Altmetric has tracked 22,836,570 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,288 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 86% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 389,033 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 153 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.