상세정보
Export to Refworks부가기능
Learning High-order Relations for Network-based Phenome-genome Association Analysis
상세 프로파일
| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Learning High-order Relations for Network-based Phenome-genome Association Analysis. |
| 개인저자 | Petegrosso, Raphael. |
| 단체저자명 | University of Minnesota. Computer Science. |
| 발행사항 | [S.l.]: University of Minnesota., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 108 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-04B. Dissertation Abstract International |
| ISBN | 9781687962034 |
| 학위논문주기 | Thesis (Ph.D.)--University of Minnesota, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Advisor: Kuang, Rui. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | An organism's phenome is the expression of characteristics from genetic inheritance and interaction with the environment. This includes simple physical appearance and traits, and even complex diseases. In human, the understanding of the relationship of such features with genetic markers gives insights into the mechanisms involved in the expression, and can also help to design targeted therapies and new drugs. In other species, such as plants, correlation of phenotypes with genetic mutations and geoclimatic variables also assists in the understanding of evolutionary global diversity and important characteristics such as flowering time. In this thesis, we propose to use high-order machine learning methods to help in the analysis of phenome through the associations with biological networks and ontologies. We show that, by combining biological networks with functional annotation of genes, we can extract high-order relations to improve the discovery of new candidate associations between genes and phenotypes. We also propose to detect high-order relations among multiple genomics datasets, geoclimatic features, and interactions among genes, to find a feature representation that can be utilized to successfully predict phenotypes. Experiments using the Arabidopsis thaliana species shows that our approach does not only contribute with an accurate predictive tool, but also brings an intuitive alternative for the analysis of correlation among plant accessions, genetic markers, and geoclimatic variables.Finally, we propose a scalable approach to solve challenges inherited from the use of massive biological networks in phenome analysis. Our low-rank method can be used to process massive networks in parallel computing to enable large-scale prior knowledge to be incorporated and improve predictive power. |
| 일반주제명 | Computer science. Bioinformatics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
