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Heterogeneous multimodal biomarkers analysis for Alzheimer’s disease via Bayesian network

Overview of attention for article published in EURASIP Journal on Bioinformatics & Systems Biology, August 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)

Mentioned by

news
1 news outlet

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
27 Mendeley
Title
Heterogeneous multimodal biomarkers analysis for Alzheimer’s disease via Bayesian network
Published in
EURASIP Journal on Bioinformatics & Systems Biology, August 2016
DOI 10.1186/s13637-016-0046-9
Pubmed ID
Authors

Yan Jin, Yi Su, Xiao-Hua Zhou, Shuai Huang

Abstract

By 2050, it is estimated that the number of worldwide Alzheimer's disease (AD) patients will quadruple from the current number of 36 million, while no proven disease-modifying treatments are available. At present, the underlying disease mechanisms remain under investigation, and recent studies suggest that the disease involves multiple etiological pathways. To better understand the disease and develop treatment strategies, a number of ongoing studies including the Alzheimer's Disease Neuroimaging Initiative (ADNI) enroll many study participants and acquire a large number of biomarkers from various modalities including demographic, genotyping, fluid biomarkers, neuroimaging, neuropsychometric test, and clinical assessments. However, a systematic approach that can integrate all the collected data is lacking. The overarching goal of our study is to use machine learning techniques to understand the relationships among different biomarkers and to establish a system-level model that can better describe the interactions among biomarkers and provide superior diagnostic and prognostic information. In this pilot study, we use Bayesian network (BN) to analyze multimodal data from ADNI, including demographics, volumetric MRI, PET, genotypes, and neuropsychometric measurements and demonstrate our approach to have superior prediction accuracy.

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 22%
Student > Ph. D. Student 6 22%
Student > Doctoral Student 3 11%
Researcher 3 11%
Student > Postgraduate 2 7%
Other 7 26%
Readers by discipline Count As %
Neuroscience 6 22%
Unspecified 5 19%
Medicine and Dentistry 3 11%
Agricultural and Biological Sciences 3 11%
Computer Science 2 7%
Other 8 30%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 17 September 2016.
All research outputs
#1,929,616
of 12,457,990 outputs
Outputs from EURASIP Journal on Bioinformatics & Systems Biology
#3
of 51 outputs
Outputs of similar age
#57,180
of 264,791 outputs
Outputs of similar age from EURASIP Journal on Bioinformatics & Systems Biology
#1
of 2 outputs
Altmetric has tracked 12,457,990 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 51 research outputs from this source. They receive a mean Attention Score of 1.7. This one has done particularly well, scoring higher than 94% 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 264,791 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 77% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them