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Pattern Recognition in the Developing Maize Leaf Epidermis: Gene Network Analyses and Machine Learning Approaches
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Pattern Recognition in the Developing Maize Leaf Epidermis: Gene Network Analyses and Machine Learning Approaches. |
| 개인저자 | Qiao, Pengfei. |
| 단체저자명 | Cornell University. Plant Biology. |
| 발행사항 | [S.l.]: Cornell University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 209 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-04B. Dissertation Abstract International |
| ISBN | 9781088374269 |
| 학위논문주기 | Thesis (Ph.D.)--Cornell University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Advisor: Scanlon, Michael J. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | Biological 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. |
| 일반주제명 | Plant sciences. Computer science. Bioinformatics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
