dc.creator | Fananapazir, Nafeh | |
dc.date.accessioned | 2020-08-22T20:33:55Z | |
dc.date.available | 2008-07-31 | |
dc.date.issued | 2007-07-31 | |
dc.identifier.uri | https://etd.library.vanderbilt.edu/etd-07242007-164905 | |
dc.identifier.uri | http://hdl.handle.net/1803/13515 | |
dc.description.abstract | Mass Spectrometry (MS) is emerging as a breakthrough mass-throughput technology believed to have powerful potential for producing clinical diagnostic and prognostic models and for identifying relevant disease biomarkers. A major barrier to making mass spectrometry clinically useful – and to exploring its potential in an efficient and reliable manner – is the challenge posed by data analysis of proteomic spectra in order to produce reliable predictor models of disease and clinical outcomes.
This thesis describes the development and evaluation of a fully-automated software system (FAST-AIMS), capable of analyzing mass spectra to produce high-quality diagnostic and outcome prediction models. | |
dc.format.mimetype | application/pdf | |
dc.subject | software | |
dc.subject | mass spectrometry | |
dc.subject | bioinformatics | |
dc.subject | machine learning | |
dc.subject | cancer classification | |
dc.subject | Computational biology | |
dc.subject | Cancer -- Diagnosis -- Data processing | |
dc.title | Development and evaluation of a prototype system for automated analysis of clinical mass spectrometry data | |
dc.type | thesis | |
dc.contributor.committeeMember | Doug Hardin | |
dc.contributor.committeeMember | Daniel Liebler | |
dc.contributor.committeeMember | Shawn Levy | |
dc.contributor.committeeMember | Ioannis Tsamardinos | |
dc.contributor.committeeMember | Dean Billheimer | |
dc.type.material | text | |
thesis.degree.name | MS | |
thesis.degree.level | thesis | |
thesis.degree.discipline | Biomedical Informatics | |
thesis.degree.grantor | Vanderbilt University | |
local.embargo.terms | 2008-07-31 | |
local.embargo.lift | 2008-07-31 | |
dc.contributor.committeeChair | Constantin Aliferis | |