Abstract Details
Activity Number:
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41
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Type:
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Contributed
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Date/Time:
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Sunday, August 9, 2015 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract #315903
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View Presentation
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Title:
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Mapping Epistasis and Plasticity for Quantitative Genetic Shape Variation Using Tree-Structured Models
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Author(s):
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Xiaotian Dai* and Guifang Fu
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Companies:
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and Utah State University
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Keywords:
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Random Forests ;
Classification and Regression Trees ;
Radius-Centroid-Contour ;
Variable Importance Measure
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Abstract:
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Morphological shape trait has long been a focus of many disciplines, but searching for the complex genetic and environmental mechanisms regulating shape variation is opening unprecedented challenges for statistical modeling and computational biology. The question of what are the respective roles of gene and environment and how they interplay to tune leaf shape traits has been an inextricable and controversial evolutionary topic. In this article, we explore tree-based statistical models to characterize the joint multi-genetic, gene-gene and gene-environmental interactive effects on the high dimensional shape curves and discover their relative importance for shape traits. In addition, we apply visualization methods to demonstrate the detailed genetic and environmental effects on shape variation and provide vivid visualization interpretations for the model results. This study is an extension of a previously published real leaf shape project for Populus szechuanica var tibetica and result in several exciting characteristics that were not detected before.
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Authors who are presenting talks have a * after their name.
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