Dissertation/Thesis Abstract

High-throughput analysis and advanced search for visual-observed phenotypes
by Green, Jason M., Ph.D., University of Missouri - Columbia, 2012, 195; 3576097
Abstract (Summary)

The trend in many scientific disciplines today, especially in biology and genetics, is towards larger scale experiments in which a tremendous amount of data is generated. As imaging of data becomes increasingly more popular especially in experiments related to traits and phenotypes, the ability to perform high-throughput big data analyses and to efficiently locate specific types of information within these data based on increasingly complicated and varying search criteria is of great importance to researchers and scientists. This research develops several methods for high-throughput phenotype analysis, which notably includes a two-tier floating approach to variable object pattern matching, thereby facilitating in-depth quantitative temporal analysis of lesion burden progression. This algorithm utilizes spatial configurations to address three tasks: 1) handling difficulties in registering multiple indistinct and dynamic objects across images, 2) handling variations in translation, rotation, and local distortion, and 3) detecting the presence of missing or extra objects. These methods aim to produce accurate and unique trait data that can help researchers gain further understanding and insight into the underlying biological and genetic mechanisms that mediate phenotypic expression. Research accomplishments also resulted in a number of unique advanced search mechanisms including a retrieval engine that integrates multiple phenotype text sources and domain ontologies to improve search results. Another new and significant search method developed herein retrieves objects based on temporal semantics and behavior. These search mechanisms represent the first of their kind in the phenotype community. While this computational framework is developed primarily for the plant community, it has potential applications in other domains including the medical field.

Indexing (document details)
Advisor: Shyu, Chi-Ren
School: University of Missouri - Columbia
School Location: United States -- Missouri
Source: DAI-B 75/03(E), Dissertation Abstracts International
Subjects: Plant biology, Computer science
Keywords: Advanced search, Growing anchor, High-throughput analysis, Plant phenotyping, Standardized imaging, Visual phenotypes
Publication Number: 3576097
ISBN: 978-1-303-55370-7
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