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dc.contributor.authorZaghlool, Shaza Basyounien_US
dc.date.accessioned2014-03-14T20:34:48Z
dc.date.available2014-03-14T20:34:48Z
dc.date.issued2008-04-30en_US
dc.identifier.otheretd-05022008-110603en_US
dc.identifier.urihttp://hdl.handle.net/10919/32110
dc.description.abstractUnderstanding the relationship between gene expression and phenotype is important in many areas of biology and medicine. Current methods for measuring gene expression such as microarrays however are invasive, require biopsy, and expensive. These factors limit experiments to low rate temporal sampling of gene expression and prevent longitudinal studies within a single subject, reducing their statistical power. Thus methods for non-invasive measurements of gene expression are an important and current topic of research. An interesting approach (Segal et al, Nature Biotechnology 25 (6) 2007) to indirect measurements of gene expression has recently been reported that uses existing imaging techniques and machine learning to estimate a function mapping image features to gene expression patterns, providing an image-derived surrogate for gene expression. However, the design of machine learning methods for this purpose is hampered by the cost of training and validation. My thesis shows that populations of artificial organisms simulating genetic variation can be used for designing machine learning approaches to decoding gene expression patterns from images. If analysis of these images proves successful, then this can be applied to real biomedical images reducing the limitations of invasive imaging. The results showed that the box counting dimension was a suitable feature extraction method yielding a classification rate of at least 90% for mutation rates up to 40%. Also, the box-counting dimension was robust in dealing with distorted images. The performance of the classifiers using the fractal dimension as features, actually, seemed more vulnerable to the mutation rate as opposed to the applied distortion level.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartETD_final.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectphenotypeen_US
dc.subjectmachine learningen_US
dc.subjectbiomorphen_US
dc.subjectgenotypeen_US
dc.titleUsing Artificial Life to Design Machine Learning Algorithms for Decoding Gene Expression Patterns from Imagesen_US
dc.typeThesisen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
dc.contributor.committeechairWyatt, Christopher L.en_US
dc.contributor.committeememberBaumann, William T.en_US
dc.contributor.committeememberXuan, Jianhua Jasonen_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-05022008-110603/en_US
dc.date.sdate2008-05-02en_US
dc.date.rdate2012-04-06
dc.date.adate2008-05-26en_US


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