Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 1 - 3 of 3
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- All Subjects: Gene Expression
- Creators: Li, Baoxin
- Creators: Neuer, Susanne
Description
Drosophila melanogaster, as an important model organism, is used to explore the mechanism which governs cell differentiation and embryonic development. Understanding the mechanism will help to reveal the effects of genes on other species or even human beings. Currently, digital camera techniques make high quality Drosophila gene expression imaging possible. On the other hand, due to the advances in biology, gene expression images which can reveal spatiotemporal patterns are generated in a high-throughput pace. Thus, an automated and efficient system that can analyze gene expression will become a necessary tool for investigating the gene functions, interactions and developmental processes. One investigation method is to compare the expression patterns of different developmental stages. Recently, however, the expression patterns are manually annotated with rough stage ranges. The work of annotation requires professional knowledge from experienced biologists. Hence, how to transfer the domain knowledge in biology into an automated system which can automatically annotate the patterns provides a challenging problem for computer scientists. In this thesis, the problem of stage annotation for Drosophila embryo is modeled in the machine learning framework. Three sparse learning algorithms and one ensemble algorithm are used to attack the problem. The sparse algorithms are Lasso, group Lasso and sparse group Lasso. The ensemble algorithm is based on a voting method. Besides that the proposed algorithms can annotate the patterns to stages instead of stage ranges with high accuracy; the decimal stage annotation algorithm presents a novel way to annotate the patterns to decimal stages. In addition, some analysis on the algorithm performance are made and corresponding explanations are given. Finally, with the proposed system, all the lateral view BDGP and FlyFish images are annotated and several interesting applications of decimal stage value are revealed.
ContributorsPan, Cheng (Author) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Farin, Gerald (Committee member) / Arizona State University (Publisher)
Created2012
Description
This thesis research focuses on phylogenetic and functional studies of microbial communities in deep-sea water, an untapped reservoir of high metabolic and genetic diversity of microorganisms. The presence of photosynthetic cyanobacteria and diatoms is an interesting and unexpected discovery during a 16S ribosomal rRNA-based community structure analyses for microbial communities in the deep-sea water of the Pacific Ocean. Both RT-PCR and qRT-PCR approaches were employed to detect expression of the genes involved in photosynthesis of photoautotrophic organisms. Positive results were obtained and further proved the functional activity of these detected photosynthetic microbes in the deep-sea. Metagenomic and metatranscriptomic data was obtained, integrated, and analyzed from deep-sea microbial communities, including both prokaryotes and eukaryotes, from four different deep-sea sites ranging from the mesopelagic to the pelagic ocean. The RNA/DNA ratio was employed as an index to show the strength of metabolic activity of deep-sea microbes. These taxonomic and functional analyses of deep-sea microbial communities revealed a `defensive' life style of microbial communities living in the deep-sea water. Pseudoalteromonas sp.WG07 was subjected to transcriptomic analysis by application of RNA-Seq technology through the transcriptomic annotation using the genomes of closely related surface-water strain Pseudoalteromonas haloplanktis TAC125 and sediment strain Pseudoalteromonas sp. SM9913. The transcriptome survey and related functional analysis of WG07 revealed unique features different from TAC125 and SM9913 and provided clues as to how it adapted to its environmental niche. Also, a comparative transcriptomic analysis of WG07 revealed transcriptome changes between its exponential and stationary growing phases.
ContributorsWu, Jieying (Author) / Meldrum, Deirdre R. (Thesis advisor) / Zhang, Weiwen (Committee member) / Abbaszadegan, Morteza (Committee member) / Neuer, Susanne (Committee member) / Arizona State University (Publisher)
Created2013
Description
Understanding the complexity of temporal and spatial characteristics of gene expression over brain development is one of the crucial research topics in neuroscience. An accurate description of the locations and expression status of relative genes requires extensive experiment resources. The Allen Developing Mouse Brain Atlas provides a large number of in situ hybridization (ISH) images of gene expression over seven different mouse brain developmental stages. Studying mouse brain models helps us understand the gene expressions in human brains. This atlas collects about thousands of genes and now they are manually annotated by biologists. Due to the high labor cost of manual annotation, investigating an efficient approach to perform automated gene expression annotation on mouse brain images becomes necessary. In this thesis, a novel efficient approach based on machine learning framework is proposed. Features are extracted from raw brain images, and both binary classification and multi-class classification models are built with some supervised learning methods. To generate features, one of the most adopted methods in current research effort is to apply the bag-of-words (BoW) algorithm. However, both the efficiency and the accuracy of BoW are not outstanding when dealing with large-scale data. Thus, an augmented sparse coding method, which is called Stochastic Coordinate Coding, is adopted to generate high-level features in this thesis. In addition, a new multi-label classification model is proposed in this thesis. Label hierarchy is built based on the given brain ontology structure. Experiments have been conducted on the atlas and the results show that this approach is efficient and classifies the images with a relatively higher accuracy.
ContributorsZhao, Xinlin (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Thesis advisor) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2016