↓ Skip to main content

Revealing Alzheimer’s disease genes spectrum in the whole-genome by machine learning

Overview of attention for article published in BMC Neurology, January 2018
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (56th percentile)

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
10 Dimensions

Readers on

mendeley
64 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Revealing Alzheimer’s disease genes spectrum in the whole-genome by machine learning
Published in
BMC Neurology, January 2018
DOI 10.1186/s12883-017-1010-3
Pubmed ID
Authors

Xiaoyan Huang, Hankui Liu, Xinming Li, Liping Guan, Jiankang Li, Laurent Christian Asker M. Tellier, Huanming Yang, Jian Wang, Jianguo Zhang

Abstract

Alzheimer's disease (AD) is an important, progressive neurodegenerative disease, with a complex genetic architecture. A key goal of biomedical research is to seek out disease risk genes, and to elucidate the function of these risk genes in the development of disease. For this purpose, expanding the AD-associated gene set is necessary. In past research, the prediction methods for AD related genes has been limited in their exploration of the target genome regions. We here present a genome-wide method for AD candidate genes predictions. We present a machine learning approach (SVM), based upon integrating gene expression data with human brain-specific gene network data, to discover the full spectrum of AD genes across the whole genome. We classified AD candidate genes with an accuracy and the area under the receiver operating characteristic (ROC) curve of 84.56% and 94%. Our approach provides a supplement for the spectrum of AD-associated genes extracted from more than 20,000 genes in a genome wide scale. In this study, we have elucidated the whole-genome spectrum of AD, using a machine learning approach. Through this method, we expect for the candidate gene catalogue to provide a more comprehensive annotation of AD for researchers.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 64 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 23%
Student > Ph. D. Student 12 19%
Student > Bachelor 8 13%
Student > Master 6 9%
Other 6 9%
Other 11 17%
Unknown 6 9%
Readers by discipline Count As %
Computer Science 13 20%
Biochemistry, Genetics and Molecular Biology 13 20%
Engineering 7 11%
Neuroscience 7 11%
Medicine and Dentistry 5 8%
Other 9 14%
Unknown 10 16%

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 12 January 2018.
All research outputs
#7,449,083
of 12,355,714 outputs
Outputs from BMC Neurology
#760
of 1,406 outputs
Outputs of similar age
#184,560
of 355,014 outputs
Outputs of similar age from BMC Neurology
#61
of 148 outputs
Altmetric has tracked 12,355,714 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 1,406 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 41st percentile – i.e., 41% 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 355,014 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 148 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.