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Autism risk classification using placental chorionic surface vascular network features

Overview of attention for article published in BMC Medical Informatics and Decision Making, December 2017
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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 (71st percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

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8 tweeters

Citations

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4 Dimensions

Readers on

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16 Mendeley
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Title
Autism risk classification using placental chorionic surface vascular network features
Published in
BMC Medical Informatics and Decision Making, December 2017
DOI 10.1186/s12911-017-0564-8
Pubmed ID
Authors

Jen-Mei Chang, Hui Zeng, Ruxu Han, Ya-Mei Chang, Ruchit Shah, Carolyn M. Salafia, Craig Newschaffer, Richard K. Miller, Philip Katzman, Jack Moye, Margaret Fallin, Cheryl K. Walker, Lisa Croen

Abstract

Autism Spectrum Disorder (ASD) is one of the fastest-growing developmental disorders in the United States. It was hypothesized that variations in the placental chorionic surface vascular network (PCSVN) structure may reflect both the overall effects of genetic and environmentally regulated variations in branching morphogenesis within the conceptus and the fetus' vital organs. This paper provides sound evidences to support the study of ASD risks with PCSVN through a combination of feature-selection and classification algorithms. Twenty eight arterial and 8 shape-based PCSVN attributes from a high-risk ASD cohort of 89 placentas and a population-based cohort of 201 placentas were examined for ranked relevance using a modified version of the random forest algorithm, called the Boruta method. Principal component analysis (PCA) was applied to isolate principal effects of arterial growth on the fetal surface of the placenta. Linear discriminant analysis (LDA) with a 10-fold cross validation was performed to establish error statistics. The Boruta method selected 15 arterial attributes as relevant, implying the difference in high and low ASD risk can be explained by the arterial features alone. The five principal features obtained through PCA, which accounted for about 88% of the data variability, indicated that PCSVNs associated with placentas of high-risk ASD pregnancies generally had fewer branch points, thicker and less tortuous arteries, better extension to the surface boundary, and smaller branch angles than their population-based counterparts. We developed a set of methods to explain major PCSVN differences between placentas associated with high risk ASD pregnancies and those selected from the general population. The research paradigm presented can be generalized to study connections between PCSVN features and other maternal and fetal outcomes such as gestational diabetes and hypertension.

Twitter Demographics

The data shown below were collected from the profiles of 8 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 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 44%
Student > Bachelor 2 13%
Other 2 13%
Student > Ph. D. Student 2 13%
Student > Master 1 6%
Other 1 6%
Unknown 1 6%
Readers by discipline Count As %
Neuroscience 2 13%
Medicine and Dentistry 2 13%
Agricultural and Biological Sciences 2 13%
Economics, Econometrics and Finance 1 6%
Biochemistry, Genetics and Molecular Biology 1 6%
Other 5 31%
Unknown 3 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 11 January 2018.
All research outputs
#2,983,148
of 12,387,076 outputs
Outputs from BMC Medical Informatics and Decision Making
#309
of 1,118 outputs
Outputs of similar age
#100,244
of 358,158 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#38
of 112 outputs
Altmetric has tracked 12,387,076 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,118 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 71% 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 358,158 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 112 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 66% of its contemporaries.