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Journal article
Autism risk classification using placental chorionic surface vascular network features
BMC medical informatics and decision making, v 17(1), pp 162-162
06 Dec 2017
PMID: 29212472
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Background: 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.
Methods: 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.
Results: 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.
Conclusion: 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.
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Details
- Title
- Autism risk classification using placental chorionic surface vascular network features
- Creators
- Jen-Mei Chang - California State University, Long BeachHui Zeng - California State University, Long BeachRuxu Han - California State University, Long BeachYa-Mei Chang - Tamkang UniversityRuchit Shah - Placental Analytics, LLC, New Rochelle, NY, USACarolyn M. Salafia - Placental Analytics, LLC, New Rochelle, NY, USACraig Newschaffer - Drexel UniversityRichard K. Miller - University of RochesterPhilip Katzman - University of RochesterJack Moye - Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentMargaret Fallin - Johns Hopkins UniversityCheryl K. Walker - NIH, Natl Childrens Study Placenta Consortium, Bldg 10, Bethesda, MD 20892 USALisa Croen - NIH National Children’s Study Placenta Consortium, Bethesda, MD, USA
- Publication Details
- BMC medical informatics and decision making, v 17(1), pp 162-162
- Publisher
- Springer Nature
- Number of pages
- 13
- Grant note
- NCS-BIO-2-18 / National Institutes of Health; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- A.J. Drexel Autism Institute
- Web of Science ID
- WOS:000417556700004
- Scopus ID
- 2-s2.0-85045413510
- Other Identifier
- 991019168532004721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Medical Informatics