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Pattern Recognition in the Developing Maize Leaf Epidermis: Gene Network Analyses and Machine Learning Approaches

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서명/저자사항Pattern Recognition in the Developing Maize Leaf Epidermis: Gene Network Analyses and Machine Learning Approaches.
개인저자Qiao, Pengfei.
단체저자명Cornell University. Plant Biology.
발행사항[S.l.]: Cornell University., 2019.
발행사항Ann Arbor: ProQuest Dissertations & Theses, 2019.
형태사항209 p.
기본자료 저록Dissertations Abstracts International 81-04B.
Dissertation Abstract International
ISBN9781088374269
학위논문주기Thesis (Ph.D.)--Cornell University, 2019.
일반주기 Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Advisor: Scanlon, Michael J.
이용제한사항This item must not be sold to any third party vendors.
요약Biological systems contain data of high dimensions and magnitudes, including biochemistry, cellular patterning, transcriptomics, and genomics. Here I combined network analyses and machine learning, to identify developmental patterns that may be amenable to the improvement of drought tolerance. Cuticles comprise the hydrophobic lipid layer covering the aboveground plant body, and have long been a research focus into water conservation in plants. However, no prior studies have examined cuticle development across a temporal and spatial gradient in a crop plant. I used gene network analyses to correlate the biochemical/developmental gradient of cuticle components with the underlying transcriptomic transitions to identify the role of PHYTOCHROME B-mediated light signaling in cuticle development. Subsequent statistical and biochemical analysis revealed LIPID-TRANSFER PROTEINs as evolutionary novelties contributing to the emergence of cuticles in land plants. Additionally, combining the power of genome- and transcriptome-wide association studies (GWAS and TWAS), vesicular trafficking was implicated in the regulation of cuticular evaporation rate. Water loss through the leaf surface is also moderated by specialized cell types (bulliform cells) in maize. Bulliform cell ontogeny was investigated in the developing maize leaf, and a machine learning approach (convolutional neural networks) was employed to conduct high-throughput phenotyping of microscopic bulliform cell traits in 60,780 leaf epidermal glue-impression images. A subsequent GWAS analysis on bulliform cell column number and column width identified a set of gene candidates implicated to function in cell division and DNA methylation. Overall this dissertation demonstrates a multidisciplinary approach combining developmental biology, transcriptomics, quantitative genetics, machine learning, and statistical data analysis, toward a more holistic understanding of the mechanisms of water conservation in maize.
일반주제명Plant sciences.
Computer science.
Bioinformatics.
언어영어
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