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Genetic algorithm with logistic regression for prediction of progression to Alzheimer's disease

Overview of attention for article published in BMC Bioinformatics, December 2014
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2 X users
facebook
1 Facebook page

Citations

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74 Dimensions

Readers on

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124 Mendeley
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Title
Genetic algorithm with logistic regression for prediction of progression to Alzheimer's disease
Published in
BMC Bioinformatics, December 2014
DOI 10.1186/1471-2105-15-s16-s11
Pubmed ID
Authors

Piers Johnson, Luke Vandewater, William Wilson, Paul Maruff, Greg Savage, Petra Graham, Lance S Macaulay, Kathryn A Ellis, Cassandra Szoeke, Ralph N Martins, Christopher C Rowe, Colin L Masters, David Ames, Ping Zhang

Abstract

Assessment of risk and early diagnosis of Alzheimer's disease (AD) is a key to its prevention or slowing the progression of the disease. Previous research on risk factors for AD typically utilizes statistical comparison tests or stepwise selection with regression models. Outcomes of these methods tend to emphasize single risk factors rather than a combination of risk factors. However, a combination of factors, rather than any one alone, is likely to affect disease development. Genetic algorithms (GA) can be useful and efficient for searching a combination of variables for the best achievement (eg. accuracy of diagnosis), especially when the search space is large, complex or poorly understood, as in the case in prediction of AD development.

X Demographics

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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 124 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
Australia 1 <1%
Unknown 122 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 17%
Student > Master 20 16%
Researcher 16 13%
Student > Bachelor 10 8%
Other 5 4%
Other 16 13%
Unknown 36 29%
Readers by discipline Count As %
Computer Science 15 12%
Psychology 12 10%
Engineering 11 9%
Agricultural and Biological Sciences 7 6%
Biochemistry, Genetics and Molecular Biology 6 5%
Other 30 24%
Unknown 43 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 14 May 2015.
All research outputs
#14,261,094
of 23,298,349 outputs
Outputs from BMC Bioinformatics
#4,567
of 7,379 outputs
Outputs of similar age
#189,395
of 363,692 outputs
Outputs of similar age from BMC Bioinformatics
#70
of 138 outputs
Altmetric has tracked 23,298,349 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,379 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 34th percentile – i.e., 34% of its peers scored the same or lower than it.
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 363,692 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 138 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.