ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at email@example.com.
- 3 Public
- 2 Public health
- 1 Artificial intelligence
- 1 Computer Science
- 1 Deep Learning
- 1 Disease gene
- 1 GenBank
- 1 Gene prioritization
- 1 Gene regulation
- 1 Geographic information science and geodesy
- 1 Information Extraction
- 1 Machine Learning
- 1 Natural Language Processing
- 1 Pharmacovigilance
- 1 Social Media Mining
- 1 Transcription factor
- 1 geographic information extraction
- 1 geographic information retrieval
- 1 phylogeography
- 1 viroinformatics
- 1 virus surveillance
Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work …
- Lee, Jang, Gonzalez, Graciela, Ye, Jieping, et al.
- Created Date
Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks such as pharmacovigilance via the use of Natural Language Processing (NLP) techniques. One of the critical steps in information extraction pipelines is Named Entity Recognition (NER), where the mentions of entities such as diseases are located in text and their entity type are identified. However, the language in social media is highly informal, and user-expressed health-related concepts are often non-technical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and advanced …
- Nikfarjam, Azadeh, Gonzalez, Graciela, Greenes, Robert, et al.
- Created Date
Accounting for over a third of all emerging and re-emerging infections, viruses represent a major public health threat, which researchers and epidemiologists across the world have been attempting to contain for decades. Recently, genomics-based surveillance of viruses through methods such as virus phylogeography has grown into a popular tool for infectious disease monitoring. When conducting such surveillance studies, researchers need to manually retrieve geographic metadata denoting the location of infected host (LOIH) of viruses from public sequence databases such as GenBank and any publication related to their study. The large volume of semi-structured and unstructured information that must be reviewed …
- Tahsin, Tasnia, Gonzalez, Graciela, Scotch, Matthew, et al.
- Created Date